Understanding Community Rivalry on Social MediaA Case Study of Two Footballing Giants

Sopan KhoslaskhoslaadobecomAdobe ResearchBangalore India

Siddhant Aroracs5150480iitdacin

IIT DelhiNew Delhi India

Abhilash Nandynandyabhilashgmailcom

IIT KharagpurKharagpur India

Ankita Saxenaankitasonu24gmailcom

IIT RoorkeeRoorkee India

Anandhavelu Nanandvnadobecom

Adobe ResearchBangalore India

ABSTRACTDetection of hate speech in online user generated contenthas become of increasing importance in recent times Hatespeech can not only be against a particular user but alsoagainst a group Rivalry between two communities withopposing ideologies has been observed to instigate a lotof hate content on social media during controversial eventsMoreover this online hate content has been observed to havepower to shape exogenous elements like communal riots [46] In this paper we aim to analyze community rivalry in thefootball domain (Real Madrid FC vs FC Barcelona) based onthe hate content exchanged between their supporters andunderstand how events affect the relationship between theseclubs We further analyze the behavior of key instigators ofhate speech in this domain and show how they differ fromgeneral users We also perform a linguistic analysis of thehate content exchanged between rival communities Overallour work provides a data-driven analysis of the nuancesof online hate speech in the football domain that not onlyallows a deepened understanding of its social implicationsbut also its detection

CCS CONCEPTSbull Information systems rarr Social networks bull Computingmethodologiesrarr Natural language processing Neural net-works bull Social and professional topicsrarr User character-istics

KEYWORDSsocial media OSN hate speech online communities NLPtime-series causal inference

IUI Workshopsrsquo19 March 20 2019 Los Angeles USACopyright copy 2019 for the individual papers by the papersrsquo authors Copyingpermitted for private and academic purposes This volume is published andcopyrighted by its editors

ACM Reference FormatSopan Khosla Siddhant Arora Abhilash Nandy Ankita Saxenaand Anandhavelu N 2019 Understanding Community Rivalry onSocial Media A Case Study of Two Footballing Giants In JointProceedings of the ACM IUI 2019 Workshops Los Angeles USA March20 2019 8 pages

1 INTRODUCTIONSocial media is a powerful communication tool that has facil-itated easy exchange of points of view While it has enabledpeople to interact with like-minded people share informa-tion and support during a crisis [25] it has also resulted ina rise of anti-social behavior including online harassmentcyber-bullying and hate speech [14] With more and morepeople sharing web content everyday in particular on onlinesocial networks (OSNs) the amount of hate speech is alsosteadily increasing In recent years we have witnessed agrowing interest in the area of online hate speech detectionand particularly the automatization of this task Social net-working communities like Facebook and Twitter are puttingforth hateful conduct policies [15 36] to tackle this issueUser-defined communities are an essential component

of many web platforms where users express their ideasopinions and share information These communities alsofacilitate intercommunity interactions where members ofone community engage with members of another Studies ofintercommunity dynamics in the offline setting have shownthat intercommunity interactions can lead to the exchange ofinformation and ideas [3 17 29] - or they can take a negativeturn leading to conflicts [31] If the participating commu-nities have opposing views their interactions can lead toexchange of hate content that maybe directed towards thecommunityrsquos ideology (or interest team) or its membersThese online exchanges can also lead to on-the-ground com-munal violence [4 6]

In this work we present the first comparative study on theexchange of hate content during inter-community interac-tions in football fan communities This hate maybe directed

IUI Workshopsrsquo19 March 20 2019 Los Angeles USA Khosla et al

Users (Tweets)

Community GeneralMentions HS(Sall) HS(Srival)

Real Madrid FC 564770(2065846)

10105(12827) 3502 (4234)

FC Barcelona 307931(894479)

6855(12568) 1774 (2112)

Table 1 Data Statistics

towards the club in general or the members (eg players sup-porters manager etc) of that club Specifically we analyzethe rivalry between two Spanish football communities RealMadrid FC and FC Barcelona on Twitter Real madrid FCand FC Barcelona are considered two of the biggest footballclubs in the world and enjoy large support all over the worldPrior work [19] studies how members of one communityattack the members of other community on Reddit whichprovides an explicit platform for users to create and partici-pate in interest-based communities (called subreddits in caseof Reddit) However for our study we choose Twitter as itprovides a larger cross-section of general publicIn order to characterize the dynamics of hate between

these communities We first try to understand how hatespeech changes over time Specifically does hate speechincrease over time and does it spike during external eventsNext we try to understand the characteristics of users whospread hate and then we try to understand the hateful tweetsthemselves We also try to analyze how hate from the rivalcommunity is similar or dissimilar to the general hate againsta target community and its members

2 RELATEDWORKThere have been notable contributions in the area of hatedetection in Online Social Networks (OSN) and websites [2224] use lexical features like word and character n-grams av-erage word embeddings and paragraph embeddings Otherworks [6 11 21 30 37] have leveraged profane words part-of-speech tags sentiment words and insulting syntactic con-structs in pre-processing and as features for hate classifica-tionModels used in the existing literature include supervisedclassification methods such as Naive-Bayes [20] LogisticRegression [38] [10] Rule-Based Classifiers [18] RandomForests [7] and Deep Neural Networks [1]

There have been fewer efforts towards characterizing hate-ful users online (people who post hate speech in OSNs)Chatzakou et al [8] study the Twitter users in the contextof GamerGate controversy In another work Chatzakouet al [9] use a supervised model to classify Twitter usersinto four classes bully aggressive spam and normal Rudrael al [27] characterize Twitter users who post communaltweets during disaster events based on their popularity in-terests and social interactions

Silva et al [30] use regex patterns like I ltintensitygt hatelttargeted groupgt to identify hate target groups in terms oftheir class and ethnicity Their system has very low recall asthey only rely on very specific sentence structures Anotherline of work [13] identifies individual targets using mentionsin the hate tweets and uses Perspective APIrsquos toxicity andattack_on_commenter scores to detect if the hate speech isagainst the mentioned individual In this work we leveragethe prior art in the area of stance detection for target-specifichate speech detection [32]

3 BACKGROUNDWe define hate speech (HS) and hateful users (HU) accord-ing to the guidelines put forth by Twitter [36] Any contentthat promotes violence against or directly attacks or threatensother people on the basis of race ethnicity national originsexual orientation gender gender identity religious affiliationage disability or serious disease is considered as hate speechOn the other hand hateful user is a user that perpetratessuch type of content Target Community (T) is defined as theideology team interest ethnicity etc which the intendedrecipients of hate speech belong to Whereas source com-munity (S) is the ideology team interest ethnicity etc thatcharacterizes the group of users who post one or more hatetweets For example if a FC Barcelona supporter posts hateagainst Real Madrid or its members then FC Barcelona isconsidered the source community and Real Madrid is thetarget community

4 DATA COLLECTIONIn this section we provide details about the data collectionand pre-processing pipelineData Sources We collect data from three main sources Wecollect tweets from Twitter using tweepy API match fixturesand outcomes from Foxsportscom and other match statisticsfrom espncomTarget specific tweetsWe collect relevant tweets in Englishlanguage (via Twitter Search API) that mention the targetcommunity of interest from June 2017 to May 2018 Wecreate a list of entities (players managers owner etc) thatbelong to the target community of interest and use them asour search keywords for thisPreliminary hate filteringWe adopt a high recall data col-lection mechanism to represent a fair sense of hate speechin our domain Similar to [13] we use a lexicon of abusivewords adopted from [39] to retrieve English hate terms Af-ter removing phrases that are context dependent we usethe resultant list of hate words to filter the extracted targetspecific tweetsSource Community Identification We identify the com-munity to which the Twitter user belongs by extracting theirfriends We check if the user follows the official pages of

Understanding Community Rivalry on Social Media IUI Workshopsrsquo19 March 20 2019 Los Angeles USA

Figure 1 Schematic diagram of our target-specific hate speech detector

the community of interest (or its members) to categorizehim as member of that community Using this differentiationprocess we categorize users into Barcelona supporters RealMadrid supporters neither

5 TARGET-SPECIFIC HATE SPEECH DETECTIONTweets collected in the previous section merely mentionthe target community This does not guarantee that theyare actually talking about it Also despite the qualitativeinspection of keyphrases the filtered dataset still containednon-hate speech tweets To mitigate the effects of obscurecontexts in the filtering process we propose a two-step clas-sifier that would provide us with tweets that contain hatespeech against the target entity

Figure 1 shows the workflow for the proposed frameworkA tweet is first passed through the hate-speech detectionmodel If the model classifies the text as positive for hatespeech then it is input to the stance detector along withthe target entity of interest If the stance detection modelclassifies it as negative towards the target then we assertthat tweet contains hate-speech against the target entity

Does this tweet contain hate speechThere has not beenmuch prior work in modeling hate speechspecific to sports domain Therefore we use an existing hatespeech detection model trained on dataset from a differentdomain and see how it fares in our domain

We use an LSTMmodel [1] trained on two popular datasetsof general tweets manually annotated for hate speech to de-tect if hate speech is present in the tweet Dataset introducedin [16] consists of 70K tweets manually annotated as abusivehateful normal and spam whereas the dataset proposed in[38] categorizes 20K tweets into sexist racist and neitherWe consider the tweets labeled as abusive hateful sexist orracist in the datasets as positive for hate speech Spam sam-ples are not used for training We use an LSTM for modelingas it has been shown to capture the long-range dependenciesin tweets which may play a role in hate-speech detection

Is this hate directed towards the target communityStance is used to define target-specific opinion (as againsta general opinion) which can be favor against or neutralStance helps to disambiguate between the generic sentimentor opinion of an individual with what the individual is refer-ring to In this work we leverage stance detection algorithmsas the second step in our target-specific hate detector

To perform stance detection in tweets we leverage a state-of-the-art TC-LSTM model introduced by [33] for target-dependent sentiment classification

6 ANALYSISIn this section we present the analysis on the hate dynam-ics between two footballing giants - Real Madrid FC (alsoreferred to as madrid) and FC Barcelona (barca) Tweetscategorized by our model as hate speech against the targetcommunity t by users of source community s are referredto as HS(Ss Tt) and the corresponding users who postedthem as HU(Ss Tt) Furthermore Sall is used to representall hate against a target community

Is hate exchange a year round eventWe plot the time-series (Figure 2) of total number of hatefultweets exchanged between Real Madrid FC and FC Barcelonain the period ranging from June 2017 to May 2018 (footballseason 2017-18) We observe that the number of tweets withhate speech spike in isolation A close inspection maps thesespikes to football matches (also called events in rest of thepaper) in which the target community was playing In theabsence of these events we do not find a substantial amountof hate speech and hate speech in general does not seem toincrease with timeStudies [6 28] have shown that online hate speech has

increased over the years However it is difficult to addressthis question in retrospection as several offensive tweets aretaken down by Twitter soon after they are posted

IUI Workshopsrsquo19 March 20 2019 Los Angeles USA Khosla et al

0 100 200 300 400

0

200

400

600

800

1000HS(Sbarca Tmadrid)

0 100 200 300 400

0

200

400

600

800

1000HS(Smadrid Tbarca)

Figure 2 Number of hate tweets exchanged between Real Madrid FC and FC Barcelona in 2017-18 season Vertical linesdenote a sample of events during the season Green corresponds to el-classico matches black and orange lines represent

matches which saw smallest and largest amount of hate respectively from the rival community

0 100 200 300 400

0

1

2

3

4

5middot10minus2

ratio

HS(Sall Tmadrid)

0 100 200 300 400

0

02

04

06

08

1

middot10minus2

ratio

HS(Sbarca Tmadrid)

Figure 3 Ratio of lsquohate speech towards Real Madrid FCrsquo to lsquoGeneral Mentions of Real Madrid FCrsquo during the 2017-18 season

Impact of offline events on hate speech onlineFollowing the inference from the previous section we nowtry to quantify the impact an event had on online hate speechWe posit that an event (a football match in our case) has highimpact on hate speech if it results in an increased relativeamount of hate speech against non-event days To analyzethe impact of events on online hate exchange between thetwo communities of interest we plot a time-series of theratio of HS(SsTt) to general tweets mentioning target com-munity t (Figure 3 shows the time-series for Tmadrid) Wequantify their effect by treating them as interventions on ob-served time series Following [25] we use Brodersen et alrsquostechnique [5] for causal inference on time-series to quantifythe impact of football events on hate speech The behaviorof observed time-series (treatment) after a football match iscompared with a counterfactual time series (control) Sincewe do not observe the control time-series we model it fromobserved time-series (in different time ranges) that correlatewith the treatment series but were not affected by the eventFinally we use this setup to model the counterfactual of thetreated time series using difference-in-differences approach

A Observed Time-SeriesWe define our treatment series witha timespan of two days before the match as pre-treatmentperiod and two days after the match as post-treatment periodB Synthetic Control Group creationWe then identify possiblecontrol groups as time-series that occur in history duringsame days of the week as the treatment series with no eventtaking place during this time interval We rank all controlgroups based on their similarity to the treatment group usingWilcoxon signed rank test and select the top 2 ranked timeseries for creating our synthetic control time seriesC Impact Estimation We finally use the difference betweenobserved post treatment time series and the synthetic con-trol time series to calculate the impact of event The relativeincrease in online hate speech during an event is given by

relef f ect = 100 lowastsumtk minus cksumck

(1)

where tk is the value of the treatment time series at time k and ck that of the control time series

Do all events contribute to the hate speech equally Our re-sults show that outcome of matches seems to affect the hate

Understanding Community Rivalry on Social Media IUI Workshopsrsquo19 March 20 2019 Los Angeles USA

HatefulUsers(HU)

General Hate( of total tweets)

Event Overlap( of total events) Followers

Mean() Users whopost gt 10 Mean() Users who

post in gt 20 Mean Userswith lt 100

Users withgt 10 000

T madridSall 987 4373 1176 008 257058 2166 246

Sbarca 1009 4592 1145 001 206283 1829 264T barca

Sall 910 3617 1480 005 300074 3700 370Smadrid 936 3940 1384 000 106966 3045 160

Table 2 Characterizing hateful users

Figure 4 Example of impact estimation withcounterfactual predictions for the event Champions

League 2018 final between Real Madrid FC and LiverpoolFC Top Black is the observed series beforeafter the event

blue (dashed) is the counterfactual

exchange We find that losses and draws trigger greater hatespeech than wins (p lt 0001) We also observe that HomeLosses andDraws trigger greater hate in comparison to AwayLosses andDraws (p lt 0001) In contrast AwayWins triggergreater hate than Home Wins (HU(Sall Tmadrid) p lt 001HU(Sbarca Tmadrid) p lt 0001) particularly from therival community Moreover group games trigger less hatein comparison to championship games (HU(Sall Tmadrid)p = 005 HU(Sbarca Tmadrid) p lt 005) Matches whichlead to elimination (HU(Sall Tmadrid) p lt 0001 HU(SallTmadrid) p lt 005) trigger greater hate Matches playedagainst the rival community tend to bring more hate fromthe rival community (p lt 0001) More generally we findthat rival community posts disproportionately higher hatefor matches whose results directly impact them A similartrend is observed for t = barca (omitted for brevity)

Characterizing hateful usersIn this section we analyze HU(Ss Tt) Users who post hate-ful content against the target community of interest

Do they post hate speech in general We investigate if thehateful users in our domain use hate speech in their generaltweets as well We extract their 3200 most recent tweetsand classify them using our hate speech detection model(explained in the earlier sections) after excluding tweets thatmentioned entities related to Real Madrid FC or FC BarcelonaWe observe that users who post hate in our domain alsopropagate significant hate in general with around 10 oftheir general tweets being hateful (Table 2) Our results showthat HU(S rival) post more hate in general as against HU(Sall) with a higher percentage of HU(S barca T madrid)crossing the 10 mark as compared to HU(S all T madrid)(4592 vs 4373) A similar trend is observed for Tbarca

Do they post hate in multiple events We then analyze theuser overlap across different events to see if there is a com-mon set of users who post hate during multiple events Anyuser who posts in a 24hr interval after the event is assumedto have posted because of that event We find that around90HUs write hate tweets for only 1minus2 of football matchesthroughout the year Whereas less than 01 HUs post hatein more than 20 of the total football matches in 2017-18 sea-son (Table 2) Members of the rival community HU(Srival)on average show lower event overlap compared to HU(Sall)This is consistent with the findings in the Analysis sectionas the rival community is only interested in events whichimpact them directly

Are they popular Next we check if the users who writehate tweets enjoy popularity on Twitter We use numberof followers as a metric to do the popularity analysis Asshown in Table 2 target-specific hate in our domain is postedby common masses (less than 100 followers) whereas thepopular users (more than 10000 followers) seldom (lt 4)participate in this phenomenon Popular members of Real

IUI Workshopsrsquo19 March 20 2019 Los Angeles USA Khosla et al

Madrid FC community seem to avoid hate speech against FCBarcelona (16)

What are their key personality traits To study the key char-acteristics of the personalities of HUs in our domain we usethe Twitter REST API to fetch the most recent 3200 tweetsfor each account We exclude retweets as they might not re-flect authorrsquos point of view and use IBM Watson PersonalityInsights API1 for this analysis It outputs a normalized per-centile score for the characteristic We study the results of theBig Five personality model the most widely used model forgenerally describing how a person engages with the worldThe model includes five primary dimensions AgreeablenessConscientiousness Extraversion Neuroticism and Open-ness [2]Figure 5 shows the distribution of scores of the Big Five

personality traits for Tmadrid We find that HS(Sall) andHS(Sbarca) have more similar personalities to each otherthan general mentions Both HS(Sall) and HS(Sbarca) ex-hibit lower Agreeableness than general mentions Prior work[34] associates lower Agreeableness scores with suspiciousand antagonistic behaviors Our results indicate that HS(Sall)and HS(Sbarca) are more self-focused contrary cautious ofothers and lack empathy For Conscientiousness HS(Sall)and HS(Sbarca) generally have lower scores than generalmentions Our results suggest that these users are laid backless goal-oriented and tend to disregard rules Low Extraver-sion scores for both HS(Sall) and HS(Sbarca) show that theyare less sociable less assertive and more within themselvesHS(Sall) and HS(Sbarca) have slightly higher but statisti-cally significant scores for Neuroticism which indicates thatthey are more susceptible to stress and are more likely to ex-perience anxiety jealousy and anger However for Opennessthe distributions for HS(Sall) and HS(Sbarca) are close togeneral mentions (with median of approximately 019) Weobserve a similar trend for Tbarca but omit here for brevity

Characterizing hateful tweetsAre these hate tweets popular We next investigate the popu-larity of target-specific hate tweets in our domain We usethe retweet-count of tweets to judge their popularity We ob-serve that for target community FC Barcelona (Tbarca) hate-ful tweets from Real Madrid ie HS(Smadrid Tbarca) areretweeted less than general hateful tweets HS(Sall Tbarca)(with mean = 019 and 053 respectively) which in turn aresubstantially less popular than non-hateful tweets (mean =547)

Content Characteristics We use SAGE [12] to analyze salientwords that characterize different types of tweets SAGE at-tempts to find salient terms in a text (child) with respect to

1httpswwwibmcomwatsonservicespersonality-insights

Exp Top 10 Salient Words

(i) fk bch fked fking adore cnt yer kidcheating idiot

(ii) fk fked fking bch cnt bullst adoreshty sht cheating

(iii)granada rampant messi bartomeudimiteya

valverde forcabarca viscabarca yermadridiots penaldo

Table 3 Top 10 salient words learned by SAGE for differentexperiments with Tmadrid Note the presence of barca

specific keywords (bold) in (iii)

some base content (base) It creates clean topic models bytaking into account the additive effects and combines multi-ple generative facets like topic and perspective distributionof words In this analysis we conduct three experiments (i)child = HS(Sall) base = tweets which mention the targetcommunity (ii) child = HS(Srival)base = tweets which men-tion the target community and (iii) child = HS(Srival) base= HS(Sall) We look at the top 10 salient words learned forthe above-mentioned experiments (Table 3)

As a whole both (i) and (ii) contain similar salient wordsThese words are mostly cuss words which is to be expectedThe top salient words in (iii) contain mentions of the entitiesof the source community On closer look we find that thesetweets try to demean the target community (eg playersmanagers or ideology etc) in an attempt to glorify the sourcecommunity For example this hate tweet by a Real Madrid FCsupporter against FC Barcelona king dem ronaldo king demleft and right salute d kingi want to take this opportunity andsay fk all barcelona fans fcbarcelona _es tries to glorifyRonaldo (an ex Real Madrid FC player) by calling him a KingSuch a pattern of comparison is not visible in HS(Sall) whichmostly focuses on the negatives of the target communityFor example this hate tweet against FC Barcelona from auser who is not a Real Madrid FC community member DearFCBarcelona please take your sht (Bellerin) back Please

Psycholinguistic Analysis We use LIWC2015 [26] for a fullpsycholinguistic analysis We look at the following dimen-sions summary scores personal pronouns and negativeemotions2 In Figure 6 we can see that tweets with general(non-hate) mentions (NHM) of Real Madrid FC differ signifi-cantly from hateful tweets Summary scores suggest that gen-eral tweets display higher values of tone than HS(Tmadrid)suggesting that targeted hate speech is more hostile HS(Tmadrid) contains higher number of pronouns and is angrierthan general tweets Also HS(Tmadrid) is more informal andexpectedly contain more swear words It contains shortersentences and uses more dictionary words on average as

2LIWC2015 language manual [26] provides a detailed description for thesedimensions

Understanding Community Rivalry on Social Media IUI Workshopsrsquo19 March 20 2019 Los Angeles USA

Figure 5 Distribution of scores for the Big Five personality traits for users who posted tweets that mention Real Madrid FCGeneral Mentions are tweets that mention the target community or its associated entities

Tone Analytic Clout WPS Dic0

20

40

60

80

100

Score

HS(Sall) HS(Sbarca) NHM

(a) Summary

i we they you shehe0

05

1

15

2

Score

HS(Sall) HS(Sbarca) NHM

(b) Personal Pronouns

anger sad anx0

2

4

6

Score

HS(Sall) HS(Sbarca) NHM

(c) Negative Emotions

Figure 6 Psycholinguistic Analysis of tweets whichmention Real Madrid FC (Tmadrid)

against the non-hate tweets mentioning Real Madrid FC Asimilar trend is observed for Tbarca (omitted for brevity)

7 DISCUSSION AND CONCLUSIONSIn this work we provide a novel view of hate exchangebetween different communities in the football domain Wedesign a two-step model to detect target-specific hate speechUsing causal inference methodologies we are able to mea-sure the effect of external events on hate speech on social

media We show how rival communities post disproportion-ately high amount of hate during events which have a directimpact on their teamrsquos interests We find that hateful usersin our domain also post hate speech in general They donot post hate in multiple events and do not enjoy generouspopularity on social media We show that their personalitycharacteristics are significantly different from general userswho post about the target community Our analysis showsthat hate tweets from rival community members differ intheir theme from general hate tweets towards the targetcommunity They try to glorify their teamrsquos players playingstyle or ideology while demeaning the target communityHowever the psycholinguistic analysis of the hateful tweetssuggests that content from rival community does not differfrom general tweets in terms of the emotional content tonepronoun usage or swear wordsNonetheless our analysis has limitations Recent studies

by Tufekci [35] and Morstatter et al [23] have discussed thesample quality of the Twitter API Since our analysis relies onkeyword-based methods for retrieval of explicit hate speechwe cannot claim to have captured a complete representationof the hate exchange on Twitter However our main objectivewas to characterize hateful users and tweets in the sportsdomain with high precision and we believe that our carefulfiltering and classification models were able to do so

REFERENCES[1] Pinkesh Badjatiya Shashank Gupta Manish Gupta and Vasudeva

Varma 2017 Deep learning for hate speech detection in tweets Pro-ceedings of the 26th International Conference on World Wide Web Com-panion 759ndash760

IUI Workshopsrsquo19 March 20 2019 Los Angeles USA Khosla et al

[2] Murray R Barrick and Michael K Mount 1991 The big five personalitydimensions and job performance ameta-analysis Personnel psychology44 1 (1991) 1ndash26

[3] Vaacuteclav Belaacutek Samantha Lam and Conor Hayes 2012 Cross-Community Influence in Discussion Fora ICWSM 12 (2012) 34ndash41

[4] Susan Benesch 2014 Countering dangerous speech to prevent massviolence during KenyaacircĂŹs 2013 elections Final Report (2014) 1ndash26

[5] Kay H Brodersen Fabian Gallusser Jim Koehler Nicolas Remy andSteven L Scott 2015 Inferring causal impact using Bayesian structuraltime-series models Annals of Applied Statistics 9 (2015) 247ndash274

[6] Pete Burnap Omer F Rana Nick Avis Matthew Williams WilliamHousley Adam Edwards Jeffrey Morgan and Luke Sloan 2015 Detect-ing tension in online communities with computational Twitter analysisTechnological Forecasting and Social Change 95 (2015) 96ndash108

[7] Pete Burnap and Matthew L Williams 2016 Us and them identifyingcyber hate on Twitter across multiple protected characteristics EPJData Science 5 1 (23 Mar 2016) 11 httpsdoiorg101140epjdss13688-016-0072-6

[8] Despoina Chatzakou Nicolas Kourtellis Jeremy Blackburn EmilianoDe Cristofaro Gianluca Stringhini and Athena Vakali 2017 Hateis not binary Studying abusive behavior of gamergate on twitterProceedings of the 28th ACM conference on hypertext and social media65ndash74

[9] Despoina Chatzakou Nicolas Kourtellis Jeremy Blackburn EmilianoDe Cristofaro Gianluca Stringhini and Athena Vakali 2017 Meanbirds Detecting aggression and bullying on twitter Proceedings of the2017 ACM on web science conference 13ndash22

[10] Thomas Davidson Dana Warmsley Michael Macy and Ingmar Weber2017 Automated hate speech detection and the problem of offensivelanguage Proceedings of ICWSM (2017)

[11] Karthik Dinakar Birago Jones Catherine Havasi Henry Liebermanand Rosalind Picard 2012 Common sense reasoning for detectionprevention and mitigation of cyberbullying ACM Transactions onInteractive Intelligent Systems (TiiS) 2 3 (2012) 18

[12] Jacob Eisenstein Amr Ahmed and Eric P Xing 2011 Sparse additivegenerative models of text Proceedings of the 28th International Confer-ence on International Conference on Machine Learning 1041ndash1048

[13] Mai ElSherief Vivek Kulkarni Dana Nguyen William Yang Wangand Elizabeth Belding 2018 Hate Lingo A Target-based LinguisticAnalysis of Hate Speech in Social Media ICWSM (2018)

[14] Mai ElSherief Shirin Nilizadeh Dana Nguyen Giovanni Vigna andElizabeth Belding 2018 Peer to Peer Hate Hate Speech Instigatorsand Their Targets ICWSM (2018)

[15] Facebook 2016 Controversial Harmful and Hateful Speech on Face-book httpsgooglTWAHdr

[16] Antigoni-Maria Founta Constantinos Djouvas Despoina ChatzakouIlias Leontiadis Jeremy Blackburn Gianluca Stringhini Athena VakaliMichael Sirivianos and Nicolas Kourtellis 2018 Large Scale Crowd-sourcing and Characterization of Twitter Abusive Behavior Interna-tional Conference on Social Web and Media Log (2018)

[17] Howard Giles 2005 Intergroup communication Multiple perspectivesVol 2 Peter Lang

[18] Njagi Dennis Gitari Zhang Zuping Hanyurwimfura Damien andJun Long 2015 A lexicon-based approach for hate speech detectionInternational Journal of Multimedia and Ubiquitous Engineering 10 4(2015) 215ndash230

[19] Srijan Kumar William L Hamilton Jure Leskovec and Dan Jurafsky2018 Community interaction and conflict on the web Proceedings ofthe 2018 World Wide Web Conference on World Wide Web 933ndash943

[20] Irene Kwok and YuzhouWang 2013 Locate the Hate Detecting Tweetsagainst Blacks AAAI

[21] Altaf Mahmud Kazi Zubair Ahmed and Mumit Khan 2008 Detectingflames and insults in text (2008)

[22] Yashar Mehdad and Joel Tetreault 2016 Do Characters Abuse MoreThan Words Proceedings of the 17th Annual Meeting of the SpecialInterest Group on Discourse and Dialogue 299ndash303

[23] Fred Morstatter Juumlrgen Pfeffer Huan Liu and Kathleen M Carley2013 Is the Sample Good Enough Comparing Data from TwitterrsquosStreaming API with Twitterrsquos Firehose ICWSM

[24] Chikashi Nobata Joel Tetreault Achint Thomas Yashar Mehdad andYi Chang 2016 Abusive language detection in online user contentProceedings of the 25th international conference on world wide web145ndash153

[25] Alexandra Olteanu Carlos Castillo Jeremy Boy and Kush R Varshney2018 The Effect of Extremist Violence on Hateful Speech Online arXivpreprint arXiv180405704 (2018)

[26] James W Pennebaker Ryan L Boyd Kayla Jordan and Kate Black-burn 2015 The development and psychometric properties of LIWC2015Technical Report

[27] Koustav Rudra Siddhartha Banerjee Niloy Ganguly Pawan GoyalMuhammad Imran and Prasenjit Mitra 2016 Summarizing situationaltweets in crisis scenario Proceedings of the 27th ACM Conference onHypertext and Social Media 137ndash147

[28] SafeHomeorg 2017 Hate on Social Media httpswwwsafehomeorgresourceshate-on-social-media

[29] Muzafer Sherif 2015 Group conflict and co-operation Their socialpsychology Psychology Press

[30] Leandro Arauacutejo Silva Mainack Mondal Denzil Correa Fabriacutecio Ben-evenuto and Ingmar Weber 2016 Analyzing the Targets of Hate inOnline Social Media ICWSM 687ndash690

[31] Henri Tajfel and John C Turner 1979 An integrative theory of in-tergroup conflict The social psychology of intergroup relations 33 47(1979) 74

[32] Duyu Tang Bing Qin Xiaocheng Feng and Ting Liu 2016 EffectiveLSTMs for target-dependent sentiment classification InternationalConference on Computational Linguistics (2016) 3298ndash3307

[33] Duyu Tang Bing Qin Xiaocheng Feng and Ting Liu 2016 Target-Dependent Sentiment Classification with Long Short Term Mem-ory Proceedings of COLING 2016 the 26th International Conferenceon Computational Linguistics Technical Papers abs151201100 (2016)3298acircĂŞ3307

[34] Ginka Toegel and Jean-Louis Barsoux 2012 How to become a betterleader MIT Sloan Management Review 53 3 (2012) 51ndash60

[35] Zeynep Tufekci 2014 Big Questions for Social Media Big Data Repre-sentativeness Validity and Other Methodological Pitfalls ICWSM 14(2014) 505ndash514

[36] Twitter 2016 Hateful Conduct Policy httpssupporttwittercomarticles20175050

[37] Cynthia Van Hee Els Lefever Ben Verhoeven Julie Mennes BartDesmet Guy De Pauw Walter Daelemans and Veacuteronique Hoste 2015Detection and fine-grained classification of cyberbullying events In-ternational Conference Recent Advances in Natural Language Processing(RANLP) 672ndash680

[38] Zeerak Waseem and Dirk Hovy 2016 Hateful Symbols or HatefulPeople Predictive Features for Hate Speech Detection on TwitterProceedings of the NAACL Student Research Workshop 88ndash93 httpwwwaclweborganthologyN16-2013

[39] Michael Wiegand Josef Ruppenhofer Anna Schmidt and ClaytonGreenberg 2018 Inducing a Lexicon of Abusive Wordsndasha Feature-Based Approach Proceedings of the 2018 Conference of the North Amer-ican Chapter of the Association for Computational Linguistics HumanLanguage Technologies Volume 1 (Long Papers) 1 1046ndash1056

IUI Workshopsrsquo19 March 20 2019 Los Angeles USA Khosla et al

Users (Tweets)

Community GeneralMentions HS(Sall) HS(Srival)

Real Madrid FC 564770(2065846)

10105(12827) 3502 (4234)

FC Barcelona 307931(894479)

6855(12568) 1774 (2112)

Table 1 Data Statistics

towards the club in general or the members (eg players sup-porters manager etc) of that club Specifically we analyzethe rivalry between two Spanish football communities RealMadrid FC and FC Barcelona on Twitter Real madrid FCand FC Barcelona are considered two of the biggest footballclubs in the world and enjoy large support all over the worldPrior work [19] studies how members of one communityattack the members of other community on Reddit whichprovides an explicit platform for users to create and partici-pate in interest-based communities (called subreddits in caseof Reddit) However for our study we choose Twitter as itprovides a larger cross-section of general publicIn order to characterize the dynamics of hate between

these communities We first try to understand how hatespeech changes over time Specifically does hate speechincrease over time and does it spike during external eventsNext we try to understand the characteristics of users whospread hate and then we try to understand the hateful tweetsthemselves We also try to analyze how hate from the rivalcommunity is similar or dissimilar to the general hate againsta target community and its members

2 RELATEDWORKThere have been notable contributions in the area of hatedetection in Online Social Networks (OSN) and websites [2224] use lexical features like word and character n-grams av-erage word embeddings and paragraph embeddings Otherworks [6 11 21 30 37] have leveraged profane words part-of-speech tags sentiment words and insulting syntactic con-structs in pre-processing and as features for hate classifica-tionModels used in the existing literature include supervisedclassification methods such as Naive-Bayes [20] LogisticRegression [38] [10] Rule-Based Classifiers [18] RandomForests [7] and Deep Neural Networks [1]

There have been fewer efforts towards characterizing hate-ful users online (people who post hate speech in OSNs)Chatzakou et al [8] study the Twitter users in the contextof GamerGate controversy In another work Chatzakouet al [9] use a supervised model to classify Twitter usersinto four classes bully aggressive spam and normal Rudrael al [27] characterize Twitter users who post communaltweets during disaster events based on their popularity in-terests and social interactions

Silva et al [30] use regex patterns like I ltintensitygt hatelttargeted groupgt to identify hate target groups in terms oftheir class and ethnicity Their system has very low recall asthey only rely on very specific sentence structures Anotherline of work [13] identifies individual targets using mentionsin the hate tweets and uses Perspective APIrsquos toxicity andattack_on_commenter scores to detect if the hate speech isagainst the mentioned individual In this work we leveragethe prior art in the area of stance detection for target-specifichate speech detection [32]

3 BACKGROUNDWe define hate speech (HS) and hateful users (HU) accord-ing to the guidelines put forth by Twitter [36] Any contentthat promotes violence against or directly attacks or threatensother people on the basis of race ethnicity national originsexual orientation gender gender identity religious affiliationage disability or serious disease is considered as hate speechOn the other hand hateful user is a user that perpetratessuch type of content Target Community (T) is defined as theideology team interest ethnicity etc which the intendedrecipients of hate speech belong to Whereas source com-munity (S) is the ideology team interest ethnicity etc thatcharacterizes the group of users who post one or more hatetweets For example if a FC Barcelona supporter posts hateagainst Real Madrid or its members then FC Barcelona isconsidered the source community and Real Madrid is thetarget community

4 DATA COLLECTIONIn this section we provide details about the data collectionand pre-processing pipelineData Sources We collect data from three main sources Wecollect tweets from Twitter using tweepy API match fixturesand outcomes from Foxsportscom and other match statisticsfrom espncomTarget specific tweetsWe collect relevant tweets in Englishlanguage (via Twitter Search API) that mention the targetcommunity of interest from June 2017 to May 2018 Wecreate a list of entities (players managers owner etc) thatbelong to the target community of interest and use them asour search keywords for thisPreliminary hate filteringWe adopt a high recall data col-lection mechanism to represent a fair sense of hate speechin our domain Similar to [13] we use a lexicon of abusivewords adopted from [39] to retrieve English hate terms Af-ter removing phrases that are context dependent we usethe resultant list of hate words to filter the extracted targetspecific tweetsSource Community Identification We identify the com-munity to which the Twitter user belongs by extracting theirfriends We check if the user follows the official pages of

Understanding Community Rivalry on Social Media IUI Workshopsrsquo19 March 20 2019 Los Angeles USA

Figure 1 Schematic diagram of our target-specific hate speech detector

the community of interest (or its members) to categorizehim as member of that community Using this differentiationprocess we categorize users into Barcelona supporters RealMadrid supporters neither

5 TARGET-SPECIFIC HATE SPEECH DETECTIONTweets collected in the previous section merely mentionthe target community This does not guarantee that theyare actually talking about it Also despite the qualitativeinspection of keyphrases the filtered dataset still containednon-hate speech tweets To mitigate the effects of obscurecontexts in the filtering process we propose a two-step clas-sifier that would provide us with tweets that contain hatespeech against the target entity

Figure 1 shows the workflow for the proposed frameworkA tweet is first passed through the hate-speech detectionmodel If the model classifies the text as positive for hatespeech then it is input to the stance detector along withthe target entity of interest If the stance detection modelclassifies it as negative towards the target then we assertthat tweet contains hate-speech against the target entity

Does this tweet contain hate speechThere has not beenmuch prior work in modeling hate speechspecific to sports domain Therefore we use an existing hatespeech detection model trained on dataset from a differentdomain and see how it fares in our domain

We use an LSTMmodel [1] trained on two popular datasetsof general tweets manually annotated for hate speech to de-tect if hate speech is present in the tweet Dataset introducedin [16] consists of 70K tweets manually annotated as abusivehateful normal and spam whereas the dataset proposed in[38] categorizes 20K tweets into sexist racist and neitherWe consider the tweets labeled as abusive hateful sexist orracist in the datasets as positive for hate speech Spam sam-ples are not used for training We use an LSTM for modelingas it has been shown to capture the long-range dependenciesin tweets which may play a role in hate-speech detection

Is this hate directed towards the target communityStance is used to define target-specific opinion (as againsta general opinion) which can be favor against or neutralStance helps to disambiguate between the generic sentimentor opinion of an individual with what the individual is refer-ring to In this work we leverage stance detection algorithmsas the second step in our target-specific hate detector

To perform stance detection in tweets we leverage a state-of-the-art TC-LSTM model introduced by [33] for target-dependent sentiment classification

6 ANALYSISIn this section we present the analysis on the hate dynam-ics between two footballing giants - Real Madrid FC (alsoreferred to as madrid) and FC Barcelona (barca) Tweetscategorized by our model as hate speech against the targetcommunity t by users of source community s are referredto as HS(Ss Tt) and the corresponding users who postedthem as HU(Ss Tt) Furthermore Sall is used to representall hate against a target community

Is hate exchange a year round eventWe plot the time-series (Figure 2) of total number of hatefultweets exchanged between Real Madrid FC and FC Barcelonain the period ranging from June 2017 to May 2018 (footballseason 2017-18) We observe that the number of tweets withhate speech spike in isolation A close inspection maps thesespikes to football matches (also called events in rest of thepaper) in which the target community was playing In theabsence of these events we do not find a substantial amountof hate speech and hate speech in general does not seem toincrease with timeStudies [6 28] have shown that online hate speech has

increased over the years However it is difficult to addressthis question in retrospection as several offensive tweets aretaken down by Twitter soon after they are posted

IUI Workshopsrsquo19 March 20 2019 Los Angeles USA Khosla et al

0 100 200 300 400

0

200

400

600

800

1000HS(Sbarca Tmadrid)

0 100 200 300 400

0

200

400

600

800

1000HS(Smadrid Tbarca)

Figure 2 Number of hate tweets exchanged between Real Madrid FC and FC Barcelona in 2017-18 season Vertical linesdenote a sample of events during the season Green corresponds to el-classico matches black and orange lines represent

matches which saw smallest and largest amount of hate respectively from the rival community

0 100 200 300 400

0

1

2

3

4

5middot10minus2

ratio

HS(Sall Tmadrid)

0 100 200 300 400

0

02

04

06

08

1

middot10minus2

ratio

HS(Sbarca Tmadrid)

Figure 3 Ratio of lsquohate speech towards Real Madrid FCrsquo to lsquoGeneral Mentions of Real Madrid FCrsquo during the 2017-18 season

Impact of offline events on hate speech onlineFollowing the inference from the previous section we nowtry to quantify the impact an event had on online hate speechWe posit that an event (a football match in our case) has highimpact on hate speech if it results in an increased relativeamount of hate speech against non-event days To analyzethe impact of events on online hate exchange between thetwo communities of interest we plot a time-series of theratio of HS(SsTt) to general tweets mentioning target com-munity t (Figure 3 shows the time-series for Tmadrid) Wequantify their effect by treating them as interventions on ob-served time series Following [25] we use Brodersen et alrsquostechnique [5] for causal inference on time-series to quantifythe impact of football events on hate speech The behaviorof observed time-series (treatment) after a football match iscompared with a counterfactual time series (control) Sincewe do not observe the control time-series we model it fromobserved time-series (in different time ranges) that correlatewith the treatment series but were not affected by the eventFinally we use this setup to model the counterfactual of thetreated time series using difference-in-differences approach

A Observed Time-SeriesWe define our treatment series witha timespan of two days before the match as pre-treatmentperiod and two days after the match as post-treatment periodB Synthetic Control Group creationWe then identify possiblecontrol groups as time-series that occur in history duringsame days of the week as the treatment series with no eventtaking place during this time interval We rank all controlgroups based on their similarity to the treatment group usingWilcoxon signed rank test and select the top 2 ranked timeseries for creating our synthetic control time seriesC Impact Estimation We finally use the difference betweenobserved post treatment time series and the synthetic con-trol time series to calculate the impact of event The relativeincrease in online hate speech during an event is given by

relef f ect = 100 lowastsumtk minus cksumck

(1)

where tk is the value of the treatment time series at time k and ck that of the control time series

Do all events contribute to the hate speech equally Our re-sults show that outcome of matches seems to affect the hate

Understanding Community Rivalry on Social Media IUI Workshopsrsquo19 March 20 2019 Los Angeles USA

HatefulUsers(HU)

General Hate( of total tweets)

Event Overlap( of total events) Followers

Mean() Users whopost gt 10 Mean() Users who

post in gt 20 Mean Userswith lt 100

Users withgt 10 000

T madridSall 987 4373 1176 008 257058 2166 246

Sbarca 1009 4592 1145 001 206283 1829 264T barca

Sall 910 3617 1480 005 300074 3700 370Smadrid 936 3940 1384 000 106966 3045 160

Table 2 Characterizing hateful users

Figure 4 Example of impact estimation withcounterfactual predictions for the event Champions

League 2018 final between Real Madrid FC and LiverpoolFC Top Black is the observed series beforeafter the event

blue (dashed) is the counterfactual

exchange We find that losses and draws trigger greater hatespeech than wins (p lt 0001) We also observe that HomeLosses andDraws trigger greater hate in comparison to AwayLosses andDraws (p lt 0001) In contrast AwayWins triggergreater hate than Home Wins (HU(Sall Tmadrid) p lt 001HU(Sbarca Tmadrid) p lt 0001) particularly from therival community Moreover group games trigger less hatein comparison to championship games (HU(Sall Tmadrid)p = 005 HU(Sbarca Tmadrid) p lt 005) Matches whichlead to elimination (HU(Sall Tmadrid) p lt 0001 HU(SallTmadrid) p lt 005) trigger greater hate Matches playedagainst the rival community tend to bring more hate fromthe rival community (p lt 0001) More generally we findthat rival community posts disproportionately higher hatefor matches whose results directly impact them A similartrend is observed for t = barca (omitted for brevity)

Characterizing hateful usersIn this section we analyze HU(Ss Tt) Users who post hate-ful content against the target community of interest

Do they post hate speech in general We investigate if thehateful users in our domain use hate speech in their generaltweets as well We extract their 3200 most recent tweetsand classify them using our hate speech detection model(explained in the earlier sections) after excluding tweets thatmentioned entities related to Real Madrid FC or FC BarcelonaWe observe that users who post hate in our domain alsopropagate significant hate in general with around 10 oftheir general tweets being hateful (Table 2) Our results showthat HU(S rival) post more hate in general as against HU(Sall) with a higher percentage of HU(S barca T madrid)crossing the 10 mark as compared to HU(S all T madrid)(4592 vs 4373) A similar trend is observed for Tbarca

Do they post hate in multiple events We then analyze theuser overlap across different events to see if there is a com-mon set of users who post hate during multiple events Anyuser who posts in a 24hr interval after the event is assumedto have posted because of that event We find that around90HUs write hate tweets for only 1minus2 of football matchesthroughout the year Whereas less than 01 HUs post hatein more than 20 of the total football matches in 2017-18 sea-son (Table 2) Members of the rival community HU(Srival)on average show lower event overlap compared to HU(Sall)This is consistent with the findings in the Analysis sectionas the rival community is only interested in events whichimpact them directly

Are they popular Next we check if the users who writehate tweets enjoy popularity on Twitter We use numberof followers as a metric to do the popularity analysis Asshown in Table 2 target-specific hate in our domain is postedby common masses (less than 100 followers) whereas thepopular users (more than 10000 followers) seldom (lt 4)participate in this phenomenon Popular members of Real

IUI Workshopsrsquo19 March 20 2019 Los Angeles USA Khosla et al

Madrid FC community seem to avoid hate speech against FCBarcelona (16)

What are their key personality traits To study the key char-acteristics of the personalities of HUs in our domain we usethe Twitter REST API to fetch the most recent 3200 tweetsfor each account We exclude retweets as they might not re-flect authorrsquos point of view and use IBM Watson PersonalityInsights API1 for this analysis It outputs a normalized per-centile score for the characteristic We study the results of theBig Five personality model the most widely used model forgenerally describing how a person engages with the worldThe model includes five primary dimensions AgreeablenessConscientiousness Extraversion Neuroticism and Open-ness [2]Figure 5 shows the distribution of scores of the Big Five

personality traits for Tmadrid We find that HS(Sall) andHS(Sbarca) have more similar personalities to each otherthan general mentions Both HS(Sall) and HS(Sbarca) ex-hibit lower Agreeableness than general mentions Prior work[34] associates lower Agreeableness scores with suspiciousand antagonistic behaviors Our results indicate that HS(Sall)and HS(Sbarca) are more self-focused contrary cautious ofothers and lack empathy For Conscientiousness HS(Sall)and HS(Sbarca) generally have lower scores than generalmentions Our results suggest that these users are laid backless goal-oriented and tend to disregard rules Low Extraver-sion scores for both HS(Sall) and HS(Sbarca) show that theyare less sociable less assertive and more within themselvesHS(Sall) and HS(Sbarca) have slightly higher but statisti-cally significant scores for Neuroticism which indicates thatthey are more susceptible to stress and are more likely to ex-perience anxiety jealousy and anger However for Opennessthe distributions for HS(Sall) and HS(Sbarca) are close togeneral mentions (with median of approximately 019) Weobserve a similar trend for Tbarca but omit here for brevity

Characterizing hateful tweetsAre these hate tweets popular We next investigate the popu-larity of target-specific hate tweets in our domain We usethe retweet-count of tweets to judge their popularity We ob-serve that for target community FC Barcelona (Tbarca) hate-ful tweets from Real Madrid ie HS(Smadrid Tbarca) areretweeted less than general hateful tweets HS(Sall Tbarca)(with mean = 019 and 053 respectively) which in turn aresubstantially less popular than non-hateful tweets (mean =547)

Content Characteristics We use SAGE [12] to analyze salientwords that characterize different types of tweets SAGE at-tempts to find salient terms in a text (child) with respect to

1httpswwwibmcomwatsonservicespersonality-insights

Exp Top 10 Salient Words

(i) fk bch fked fking adore cnt yer kidcheating idiot

(ii) fk fked fking bch cnt bullst adoreshty sht cheating

(iii)granada rampant messi bartomeudimiteya

valverde forcabarca viscabarca yermadridiots penaldo

Table 3 Top 10 salient words learned by SAGE for differentexperiments with Tmadrid Note the presence of barca

specific keywords (bold) in (iii)

some base content (base) It creates clean topic models bytaking into account the additive effects and combines multi-ple generative facets like topic and perspective distributionof words In this analysis we conduct three experiments (i)child = HS(Sall) base = tweets which mention the targetcommunity (ii) child = HS(Srival)base = tweets which men-tion the target community and (iii) child = HS(Srival) base= HS(Sall) We look at the top 10 salient words learned forthe above-mentioned experiments (Table 3)

As a whole both (i) and (ii) contain similar salient wordsThese words are mostly cuss words which is to be expectedThe top salient words in (iii) contain mentions of the entitiesof the source community On closer look we find that thesetweets try to demean the target community (eg playersmanagers or ideology etc) in an attempt to glorify the sourcecommunity For example this hate tweet by a Real Madrid FCsupporter against FC Barcelona king dem ronaldo king demleft and right salute d kingi want to take this opportunity andsay fk all barcelona fans fcbarcelona _es tries to glorifyRonaldo (an ex Real Madrid FC player) by calling him a KingSuch a pattern of comparison is not visible in HS(Sall) whichmostly focuses on the negatives of the target communityFor example this hate tweet against FC Barcelona from auser who is not a Real Madrid FC community member DearFCBarcelona please take your sht (Bellerin) back Please

Psycholinguistic Analysis We use LIWC2015 [26] for a fullpsycholinguistic analysis We look at the following dimen-sions summary scores personal pronouns and negativeemotions2 In Figure 6 we can see that tweets with general(non-hate) mentions (NHM) of Real Madrid FC differ signifi-cantly from hateful tweets Summary scores suggest that gen-eral tweets display higher values of tone than HS(Tmadrid)suggesting that targeted hate speech is more hostile HS(Tmadrid) contains higher number of pronouns and is angrierthan general tweets Also HS(Tmadrid) is more informal andexpectedly contain more swear words It contains shortersentences and uses more dictionary words on average as

2LIWC2015 language manual [26] provides a detailed description for thesedimensions

Understanding Community Rivalry on Social Media IUI Workshopsrsquo19 March 20 2019 Los Angeles USA

Figure 5 Distribution of scores for the Big Five personality traits for users who posted tweets that mention Real Madrid FCGeneral Mentions are tweets that mention the target community or its associated entities

Tone Analytic Clout WPS Dic0

20

40

60

80

100

Score

HS(Sall) HS(Sbarca) NHM

(a) Summary

i we they you shehe0

05

1

15

2

Score

HS(Sall) HS(Sbarca) NHM

(b) Personal Pronouns

anger sad anx0

2

4

6

Score

HS(Sall) HS(Sbarca) NHM

(c) Negative Emotions

Figure 6 Psycholinguistic Analysis of tweets whichmention Real Madrid FC (Tmadrid)

against the non-hate tweets mentioning Real Madrid FC Asimilar trend is observed for Tbarca (omitted for brevity)

7 DISCUSSION AND CONCLUSIONSIn this work we provide a novel view of hate exchangebetween different communities in the football domain Wedesign a two-step model to detect target-specific hate speechUsing causal inference methodologies we are able to mea-sure the effect of external events on hate speech on social

media We show how rival communities post disproportion-ately high amount of hate during events which have a directimpact on their teamrsquos interests We find that hateful usersin our domain also post hate speech in general They donot post hate in multiple events and do not enjoy generouspopularity on social media We show that their personalitycharacteristics are significantly different from general userswho post about the target community Our analysis showsthat hate tweets from rival community members differ intheir theme from general hate tweets towards the targetcommunity They try to glorify their teamrsquos players playingstyle or ideology while demeaning the target communityHowever the psycholinguistic analysis of the hateful tweetssuggests that content from rival community does not differfrom general tweets in terms of the emotional content tonepronoun usage or swear wordsNonetheless our analysis has limitations Recent studies

by Tufekci [35] and Morstatter et al [23] have discussed thesample quality of the Twitter API Since our analysis relies onkeyword-based methods for retrieval of explicit hate speechwe cannot claim to have captured a complete representationof the hate exchange on Twitter However our main objectivewas to characterize hateful users and tweets in the sportsdomain with high precision and we believe that our carefulfiltering and classification models were able to do so

REFERENCES[1] Pinkesh Badjatiya Shashank Gupta Manish Gupta and Vasudeva

Varma 2017 Deep learning for hate speech detection in tweets Pro-ceedings of the 26th International Conference on World Wide Web Com-panion 759ndash760

IUI Workshopsrsquo19 March 20 2019 Los Angeles USA Khosla et al

[2] Murray R Barrick and Michael K Mount 1991 The big five personalitydimensions and job performance ameta-analysis Personnel psychology44 1 (1991) 1ndash26

[3] Vaacuteclav Belaacutek Samantha Lam and Conor Hayes 2012 Cross-Community Influence in Discussion Fora ICWSM 12 (2012) 34ndash41

[4] Susan Benesch 2014 Countering dangerous speech to prevent massviolence during KenyaacircĂŹs 2013 elections Final Report (2014) 1ndash26

[5] Kay H Brodersen Fabian Gallusser Jim Koehler Nicolas Remy andSteven L Scott 2015 Inferring causal impact using Bayesian structuraltime-series models Annals of Applied Statistics 9 (2015) 247ndash274

[6] Pete Burnap Omer F Rana Nick Avis Matthew Williams WilliamHousley Adam Edwards Jeffrey Morgan and Luke Sloan 2015 Detect-ing tension in online communities with computational Twitter analysisTechnological Forecasting and Social Change 95 (2015) 96ndash108

[7] Pete Burnap and Matthew L Williams 2016 Us and them identifyingcyber hate on Twitter across multiple protected characteristics EPJData Science 5 1 (23 Mar 2016) 11 httpsdoiorg101140epjdss13688-016-0072-6

[8] Despoina Chatzakou Nicolas Kourtellis Jeremy Blackburn EmilianoDe Cristofaro Gianluca Stringhini and Athena Vakali 2017 Hateis not binary Studying abusive behavior of gamergate on twitterProceedings of the 28th ACM conference on hypertext and social media65ndash74

[9] Despoina Chatzakou Nicolas Kourtellis Jeremy Blackburn EmilianoDe Cristofaro Gianluca Stringhini and Athena Vakali 2017 Meanbirds Detecting aggression and bullying on twitter Proceedings of the2017 ACM on web science conference 13ndash22

[10] Thomas Davidson Dana Warmsley Michael Macy and Ingmar Weber2017 Automated hate speech detection and the problem of offensivelanguage Proceedings of ICWSM (2017)

[11] Karthik Dinakar Birago Jones Catherine Havasi Henry Liebermanand Rosalind Picard 2012 Common sense reasoning for detectionprevention and mitigation of cyberbullying ACM Transactions onInteractive Intelligent Systems (TiiS) 2 3 (2012) 18

[12] Jacob Eisenstein Amr Ahmed and Eric P Xing 2011 Sparse additivegenerative models of text Proceedings of the 28th International Confer-ence on International Conference on Machine Learning 1041ndash1048

[13] Mai ElSherief Vivek Kulkarni Dana Nguyen William Yang Wangand Elizabeth Belding 2018 Hate Lingo A Target-based LinguisticAnalysis of Hate Speech in Social Media ICWSM (2018)

[14] Mai ElSherief Shirin Nilizadeh Dana Nguyen Giovanni Vigna andElizabeth Belding 2018 Peer to Peer Hate Hate Speech Instigatorsand Their Targets ICWSM (2018)

[15] Facebook 2016 Controversial Harmful and Hateful Speech on Face-book httpsgooglTWAHdr

[16] Antigoni-Maria Founta Constantinos Djouvas Despoina ChatzakouIlias Leontiadis Jeremy Blackburn Gianluca Stringhini Athena VakaliMichael Sirivianos and Nicolas Kourtellis 2018 Large Scale Crowd-sourcing and Characterization of Twitter Abusive Behavior Interna-tional Conference on Social Web and Media Log (2018)

[17] Howard Giles 2005 Intergroup communication Multiple perspectivesVol 2 Peter Lang

[18] Njagi Dennis Gitari Zhang Zuping Hanyurwimfura Damien andJun Long 2015 A lexicon-based approach for hate speech detectionInternational Journal of Multimedia and Ubiquitous Engineering 10 4(2015) 215ndash230

[19] Srijan Kumar William L Hamilton Jure Leskovec and Dan Jurafsky2018 Community interaction and conflict on the web Proceedings ofthe 2018 World Wide Web Conference on World Wide Web 933ndash943

[20] Irene Kwok and YuzhouWang 2013 Locate the Hate Detecting Tweetsagainst Blacks AAAI

[21] Altaf Mahmud Kazi Zubair Ahmed and Mumit Khan 2008 Detectingflames and insults in text (2008)

[22] Yashar Mehdad and Joel Tetreault 2016 Do Characters Abuse MoreThan Words Proceedings of the 17th Annual Meeting of the SpecialInterest Group on Discourse and Dialogue 299ndash303

[23] Fred Morstatter Juumlrgen Pfeffer Huan Liu and Kathleen M Carley2013 Is the Sample Good Enough Comparing Data from TwitterrsquosStreaming API with Twitterrsquos Firehose ICWSM

[24] Chikashi Nobata Joel Tetreault Achint Thomas Yashar Mehdad andYi Chang 2016 Abusive language detection in online user contentProceedings of the 25th international conference on world wide web145ndash153

[25] Alexandra Olteanu Carlos Castillo Jeremy Boy and Kush R Varshney2018 The Effect of Extremist Violence on Hateful Speech Online arXivpreprint arXiv180405704 (2018)

[26] James W Pennebaker Ryan L Boyd Kayla Jordan and Kate Black-burn 2015 The development and psychometric properties of LIWC2015Technical Report

[27] Koustav Rudra Siddhartha Banerjee Niloy Ganguly Pawan GoyalMuhammad Imran and Prasenjit Mitra 2016 Summarizing situationaltweets in crisis scenario Proceedings of the 27th ACM Conference onHypertext and Social Media 137ndash147

[28] SafeHomeorg 2017 Hate on Social Media httpswwwsafehomeorgresourceshate-on-social-media

[29] Muzafer Sherif 2015 Group conflict and co-operation Their socialpsychology Psychology Press

[30] Leandro Arauacutejo Silva Mainack Mondal Denzil Correa Fabriacutecio Ben-evenuto and Ingmar Weber 2016 Analyzing the Targets of Hate inOnline Social Media ICWSM 687ndash690

[31] Henri Tajfel and John C Turner 1979 An integrative theory of in-tergroup conflict The social psychology of intergroup relations 33 47(1979) 74

[32] Duyu Tang Bing Qin Xiaocheng Feng and Ting Liu 2016 EffectiveLSTMs for target-dependent sentiment classification InternationalConference on Computational Linguistics (2016) 3298ndash3307

[33] Duyu Tang Bing Qin Xiaocheng Feng and Ting Liu 2016 Target-Dependent Sentiment Classification with Long Short Term Mem-ory Proceedings of COLING 2016 the 26th International Conferenceon Computational Linguistics Technical Papers abs151201100 (2016)3298acircĂŞ3307

[34] Ginka Toegel and Jean-Louis Barsoux 2012 How to become a betterleader MIT Sloan Management Review 53 3 (2012) 51ndash60

[35] Zeynep Tufekci 2014 Big Questions for Social Media Big Data Repre-sentativeness Validity and Other Methodological Pitfalls ICWSM 14(2014) 505ndash514

[36] Twitter 2016 Hateful Conduct Policy httpssupporttwittercomarticles20175050

[37] Cynthia Van Hee Els Lefever Ben Verhoeven Julie Mennes BartDesmet Guy De Pauw Walter Daelemans and Veacuteronique Hoste 2015Detection and fine-grained classification of cyberbullying events In-ternational Conference Recent Advances in Natural Language Processing(RANLP) 672ndash680

[38] Zeerak Waseem and Dirk Hovy 2016 Hateful Symbols or HatefulPeople Predictive Features for Hate Speech Detection on TwitterProceedings of the NAACL Student Research Workshop 88ndash93 httpwwwaclweborganthologyN16-2013

[39] Michael Wiegand Josef Ruppenhofer Anna Schmidt and ClaytonGreenberg 2018 Inducing a Lexicon of Abusive Wordsndasha Feature-Based Approach Proceedings of the 2018 Conference of the North Amer-ican Chapter of the Association for Computational Linguistics HumanLanguage Technologies Volume 1 (Long Papers) 1 1046ndash1056

Understanding Community Rivalry on Social Media IUI Workshopsrsquo19 March 20 2019 Los Angeles USA

Figure 1 Schematic diagram of our target-specific hate speech detector

the community of interest (or its members) to categorizehim as member of that community Using this differentiationprocess we categorize users into Barcelona supporters RealMadrid supporters neither

5 TARGET-SPECIFIC HATE SPEECH DETECTIONTweets collected in the previous section merely mentionthe target community This does not guarantee that theyare actually talking about it Also despite the qualitativeinspection of keyphrases the filtered dataset still containednon-hate speech tweets To mitigate the effects of obscurecontexts in the filtering process we propose a two-step clas-sifier that would provide us with tweets that contain hatespeech against the target entity

Figure 1 shows the workflow for the proposed frameworkA tweet is first passed through the hate-speech detectionmodel If the model classifies the text as positive for hatespeech then it is input to the stance detector along withthe target entity of interest If the stance detection modelclassifies it as negative towards the target then we assertthat tweet contains hate-speech against the target entity

Does this tweet contain hate speechThere has not beenmuch prior work in modeling hate speechspecific to sports domain Therefore we use an existing hatespeech detection model trained on dataset from a differentdomain and see how it fares in our domain

We use an LSTMmodel [1] trained on two popular datasetsof general tweets manually annotated for hate speech to de-tect if hate speech is present in the tweet Dataset introducedin [16] consists of 70K tweets manually annotated as abusivehateful normal and spam whereas the dataset proposed in[38] categorizes 20K tweets into sexist racist and neitherWe consider the tweets labeled as abusive hateful sexist orracist in the datasets as positive for hate speech Spam sam-ples are not used for training We use an LSTM for modelingas it has been shown to capture the long-range dependenciesin tweets which may play a role in hate-speech detection

Is this hate directed towards the target communityStance is used to define target-specific opinion (as againsta general opinion) which can be favor against or neutralStance helps to disambiguate between the generic sentimentor opinion of an individual with what the individual is refer-ring to In this work we leverage stance detection algorithmsas the second step in our target-specific hate detector

To perform stance detection in tweets we leverage a state-of-the-art TC-LSTM model introduced by [33] for target-dependent sentiment classification

6 ANALYSISIn this section we present the analysis on the hate dynam-ics between two footballing giants - Real Madrid FC (alsoreferred to as madrid) and FC Barcelona (barca) Tweetscategorized by our model as hate speech against the targetcommunity t by users of source community s are referredto as HS(Ss Tt) and the corresponding users who postedthem as HU(Ss Tt) Furthermore Sall is used to representall hate against a target community

Is hate exchange a year round eventWe plot the time-series (Figure 2) of total number of hatefultweets exchanged between Real Madrid FC and FC Barcelonain the period ranging from June 2017 to May 2018 (footballseason 2017-18) We observe that the number of tweets withhate speech spike in isolation A close inspection maps thesespikes to football matches (also called events in rest of thepaper) in which the target community was playing In theabsence of these events we do not find a substantial amountof hate speech and hate speech in general does not seem toincrease with timeStudies [6 28] have shown that online hate speech has

increased over the years However it is difficult to addressthis question in retrospection as several offensive tweets aretaken down by Twitter soon after they are posted

IUI Workshopsrsquo19 March 20 2019 Los Angeles USA Khosla et al

0 100 200 300 400

0

200

400

600

800

1000HS(Sbarca Tmadrid)

0 100 200 300 400

0

200

400

600

800

1000HS(Smadrid Tbarca)

Figure 2 Number of hate tweets exchanged between Real Madrid FC and FC Barcelona in 2017-18 season Vertical linesdenote a sample of events during the season Green corresponds to el-classico matches black and orange lines represent

matches which saw smallest and largest amount of hate respectively from the rival community

0 100 200 300 400

0

1

2

3

4

5middot10minus2

ratio

HS(Sall Tmadrid)

0 100 200 300 400

0

02

04

06

08

1

middot10minus2

ratio

HS(Sbarca Tmadrid)

Figure 3 Ratio of lsquohate speech towards Real Madrid FCrsquo to lsquoGeneral Mentions of Real Madrid FCrsquo during the 2017-18 season

Impact of offline events on hate speech onlineFollowing the inference from the previous section we nowtry to quantify the impact an event had on online hate speechWe posit that an event (a football match in our case) has highimpact on hate speech if it results in an increased relativeamount of hate speech against non-event days To analyzethe impact of events on online hate exchange between thetwo communities of interest we plot a time-series of theratio of HS(SsTt) to general tweets mentioning target com-munity t (Figure 3 shows the time-series for Tmadrid) Wequantify their effect by treating them as interventions on ob-served time series Following [25] we use Brodersen et alrsquostechnique [5] for causal inference on time-series to quantifythe impact of football events on hate speech The behaviorof observed time-series (treatment) after a football match iscompared with a counterfactual time series (control) Sincewe do not observe the control time-series we model it fromobserved time-series (in different time ranges) that correlatewith the treatment series but were not affected by the eventFinally we use this setup to model the counterfactual of thetreated time series using difference-in-differences approach

A Observed Time-SeriesWe define our treatment series witha timespan of two days before the match as pre-treatmentperiod and two days after the match as post-treatment periodB Synthetic Control Group creationWe then identify possiblecontrol groups as time-series that occur in history duringsame days of the week as the treatment series with no eventtaking place during this time interval We rank all controlgroups based on their similarity to the treatment group usingWilcoxon signed rank test and select the top 2 ranked timeseries for creating our synthetic control time seriesC Impact Estimation We finally use the difference betweenobserved post treatment time series and the synthetic con-trol time series to calculate the impact of event The relativeincrease in online hate speech during an event is given by

relef f ect = 100 lowastsumtk minus cksumck

(1)

where tk is the value of the treatment time series at time k and ck that of the control time series

Do all events contribute to the hate speech equally Our re-sults show that outcome of matches seems to affect the hate

Understanding Community Rivalry on Social Media IUI Workshopsrsquo19 March 20 2019 Los Angeles USA

HatefulUsers(HU)

General Hate( of total tweets)

Event Overlap( of total events) Followers

Mean() Users whopost gt 10 Mean() Users who

post in gt 20 Mean Userswith lt 100

Users withgt 10 000

T madridSall 987 4373 1176 008 257058 2166 246

Sbarca 1009 4592 1145 001 206283 1829 264T barca

Sall 910 3617 1480 005 300074 3700 370Smadrid 936 3940 1384 000 106966 3045 160

Table 2 Characterizing hateful users

Figure 4 Example of impact estimation withcounterfactual predictions for the event Champions

League 2018 final between Real Madrid FC and LiverpoolFC Top Black is the observed series beforeafter the event

blue (dashed) is the counterfactual

exchange We find that losses and draws trigger greater hatespeech than wins (p lt 0001) We also observe that HomeLosses andDraws trigger greater hate in comparison to AwayLosses andDraws (p lt 0001) In contrast AwayWins triggergreater hate than Home Wins (HU(Sall Tmadrid) p lt 001HU(Sbarca Tmadrid) p lt 0001) particularly from therival community Moreover group games trigger less hatein comparison to championship games (HU(Sall Tmadrid)p = 005 HU(Sbarca Tmadrid) p lt 005) Matches whichlead to elimination (HU(Sall Tmadrid) p lt 0001 HU(SallTmadrid) p lt 005) trigger greater hate Matches playedagainst the rival community tend to bring more hate fromthe rival community (p lt 0001) More generally we findthat rival community posts disproportionately higher hatefor matches whose results directly impact them A similartrend is observed for t = barca (omitted for brevity)

Characterizing hateful usersIn this section we analyze HU(Ss Tt) Users who post hate-ful content against the target community of interest

Do they post hate speech in general We investigate if thehateful users in our domain use hate speech in their generaltweets as well We extract their 3200 most recent tweetsand classify them using our hate speech detection model(explained in the earlier sections) after excluding tweets thatmentioned entities related to Real Madrid FC or FC BarcelonaWe observe that users who post hate in our domain alsopropagate significant hate in general with around 10 oftheir general tweets being hateful (Table 2) Our results showthat HU(S rival) post more hate in general as against HU(Sall) with a higher percentage of HU(S barca T madrid)crossing the 10 mark as compared to HU(S all T madrid)(4592 vs 4373) A similar trend is observed for Tbarca

Do they post hate in multiple events We then analyze theuser overlap across different events to see if there is a com-mon set of users who post hate during multiple events Anyuser who posts in a 24hr interval after the event is assumedto have posted because of that event We find that around90HUs write hate tweets for only 1minus2 of football matchesthroughout the year Whereas less than 01 HUs post hatein more than 20 of the total football matches in 2017-18 sea-son (Table 2) Members of the rival community HU(Srival)on average show lower event overlap compared to HU(Sall)This is consistent with the findings in the Analysis sectionas the rival community is only interested in events whichimpact them directly

Are they popular Next we check if the users who writehate tweets enjoy popularity on Twitter We use numberof followers as a metric to do the popularity analysis Asshown in Table 2 target-specific hate in our domain is postedby common masses (less than 100 followers) whereas thepopular users (more than 10000 followers) seldom (lt 4)participate in this phenomenon Popular members of Real

IUI Workshopsrsquo19 March 20 2019 Los Angeles USA Khosla et al

Madrid FC community seem to avoid hate speech against FCBarcelona (16)

What are their key personality traits To study the key char-acteristics of the personalities of HUs in our domain we usethe Twitter REST API to fetch the most recent 3200 tweetsfor each account We exclude retweets as they might not re-flect authorrsquos point of view and use IBM Watson PersonalityInsights API1 for this analysis It outputs a normalized per-centile score for the characteristic We study the results of theBig Five personality model the most widely used model forgenerally describing how a person engages with the worldThe model includes five primary dimensions AgreeablenessConscientiousness Extraversion Neuroticism and Open-ness [2]Figure 5 shows the distribution of scores of the Big Five

personality traits for Tmadrid We find that HS(Sall) andHS(Sbarca) have more similar personalities to each otherthan general mentions Both HS(Sall) and HS(Sbarca) ex-hibit lower Agreeableness than general mentions Prior work[34] associates lower Agreeableness scores with suspiciousand antagonistic behaviors Our results indicate that HS(Sall)and HS(Sbarca) are more self-focused contrary cautious ofothers and lack empathy For Conscientiousness HS(Sall)and HS(Sbarca) generally have lower scores than generalmentions Our results suggest that these users are laid backless goal-oriented and tend to disregard rules Low Extraver-sion scores for both HS(Sall) and HS(Sbarca) show that theyare less sociable less assertive and more within themselvesHS(Sall) and HS(Sbarca) have slightly higher but statisti-cally significant scores for Neuroticism which indicates thatthey are more susceptible to stress and are more likely to ex-perience anxiety jealousy and anger However for Opennessthe distributions for HS(Sall) and HS(Sbarca) are close togeneral mentions (with median of approximately 019) Weobserve a similar trend for Tbarca but omit here for brevity

Characterizing hateful tweetsAre these hate tweets popular We next investigate the popu-larity of target-specific hate tweets in our domain We usethe retweet-count of tweets to judge their popularity We ob-serve that for target community FC Barcelona (Tbarca) hate-ful tweets from Real Madrid ie HS(Smadrid Tbarca) areretweeted less than general hateful tweets HS(Sall Tbarca)(with mean = 019 and 053 respectively) which in turn aresubstantially less popular than non-hateful tweets (mean =547)

Content Characteristics We use SAGE [12] to analyze salientwords that characterize different types of tweets SAGE at-tempts to find salient terms in a text (child) with respect to

1httpswwwibmcomwatsonservicespersonality-insights

Exp Top 10 Salient Words

(i) fk bch fked fking adore cnt yer kidcheating idiot

(ii) fk fked fking bch cnt bullst adoreshty sht cheating

(iii)granada rampant messi bartomeudimiteya

valverde forcabarca viscabarca yermadridiots penaldo

Table 3 Top 10 salient words learned by SAGE for differentexperiments with Tmadrid Note the presence of barca

specific keywords (bold) in (iii)

some base content (base) It creates clean topic models bytaking into account the additive effects and combines multi-ple generative facets like topic and perspective distributionof words In this analysis we conduct three experiments (i)child = HS(Sall) base = tweets which mention the targetcommunity (ii) child = HS(Srival)base = tweets which men-tion the target community and (iii) child = HS(Srival) base= HS(Sall) We look at the top 10 salient words learned forthe above-mentioned experiments (Table 3)

As a whole both (i) and (ii) contain similar salient wordsThese words are mostly cuss words which is to be expectedThe top salient words in (iii) contain mentions of the entitiesof the source community On closer look we find that thesetweets try to demean the target community (eg playersmanagers or ideology etc) in an attempt to glorify the sourcecommunity For example this hate tweet by a Real Madrid FCsupporter against FC Barcelona king dem ronaldo king demleft and right salute d kingi want to take this opportunity andsay fk all barcelona fans fcbarcelona _es tries to glorifyRonaldo (an ex Real Madrid FC player) by calling him a KingSuch a pattern of comparison is not visible in HS(Sall) whichmostly focuses on the negatives of the target communityFor example this hate tweet against FC Barcelona from auser who is not a Real Madrid FC community member DearFCBarcelona please take your sht (Bellerin) back Please

Psycholinguistic Analysis We use LIWC2015 [26] for a fullpsycholinguistic analysis We look at the following dimen-sions summary scores personal pronouns and negativeemotions2 In Figure 6 we can see that tweets with general(non-hate) mentions (NHM) of Real Madrid FC differ signifi-cantly from hateful tweets Summary scores suggest that gen-eral tweets display higher values of tone than HS(Tmadrid)suggesting that targeted hate speech is more hostile HS(Tmadrid) contains higher number of pronouns and is angrierthan general tweets Also HS(Tmadrid) is more informal andexpectedly contain more swear words It contains shortersentences and uses more dictionary words on average as

2LIWC2015 language manual [26] provides a detailed description for thesedimensions

Understanding Community Rivalry on Social Media IUI Workshopsrsquo19 March 20 2019 Los Angeles USA

Figure 5 Distribution of scores for the Big Five personality traits for users who posted tweets that mention Real Madrid FCGeneral Mentions are tweets that mention the target community or its associated entities

Tone Analytic Clout WPS Dic0

20

40

60

80

100

Score

HS(Sall) HS(Sbarca) NHM

(a) Summary

i we they you shehe0

05

1

15

2

Score

HS(Sall) HS(Sbarca) NHM

(b) Personal Pronouns

anger sad anx0

2

4

6

Score

HS(Sall) HS(Sbarca) NHM

(c) Negative Emotions

Figure 6 Psycholinguistic Analysis of tweets whichmention Real Madrid FC (Tmadrid)

against the non-hate tweets mentioning Real Madrid FC Asimilar trend is observed for Tbarca (omitted for brevity)

7 DISCUSSION AND CONCLUSIONSIn this work we provide a novel view of hate exchangebetween different communities in the football domain Wedesign a two-step model to detect target-specific hate speechUsing causal inference methodologies we are able to mea-sure the effect of external events on hate speech on social

media We show how rival communities post disproportion-ately high amount of hate during events which have a directimpact on their teamrsquos interests We find that hateful usersin our domain also post hate speech in general They donot post hate in multiple events and do not enjoy generouspopularity on social media We show that their personalitycharacteristics are significantly different from general userswho post about the target community Our analysis showsthat hate tweets from rival community members differ intheir theme from general hate tweets towards the targetcommunity They try to glorify their teamrsquos players playingstyle or ideology while demeaning the target communityHowever the psycholinguistic analysis of the hateful tweetssuggests that content from rival community does not differfrom general tweets in terms of the emotional content tonepronoun usage or swear wordsNonetheless our analysis has limitations Recent studies

by Tufekci [35] and Morstatter et al [23] have discussed thesample quality of the Twitter API Since our analysis relies onkeyword-based methods for retrieval of explicit hate speechwe cannot claim to have captured a complete representationof the hate exchange on Twitter However our main objectivewas to characterize hateful users and tweets in the sportsdomain with high precision and we believe that our carefulfiltering and classification models were able to do so

REFERENCES[1] Pinkesh Badjatiya Shashank Gupta Manish Gupta and Vasudeva

Varma 2017 Deep learning for hate speech detection in tweets Pro-ceedings of the 26th International Conference on World Wide Web Com-panion 759ndash760

IUI Workshopsrsquo19 March 20 2019 Los Angeles USA Khosla et al

[2] Murray R Barrick and Michael K Mount 1991 The big five personalitydimensions and job performance ameta-analysis Personnel psychology44 1 (1991) 1ndash26

[3] Vaacuteclav Belaacutek Samantha Lam and Conor Hayes 2012 Cross-Community Influence in Discussion Fora ICWSM 12 (2012) 34ndash41

[4] Susan Benesch 2014 Countering dangerous speech to prevent massviolence during KenyaacircĂŹs 2013 elections Final Report (2014) 1ndash26

[5] Kay H Brodersen Fabian Gallusser Jim Koehler Nicolas Remy andSteven L Scott 2015 Inferring causal impact using Bayesian structuraltime-series models Annals of Applied Statistics 9 (2015) 247ndash274

[6] Pete Burnap Omer F Rana Nick Avis Matthew Williams WilliamHousley Adam Edwards Jeffrey Morgan and Luke Sloan 2015 Detect-ing tension in online communities with computational Twitter analysisTechnological Forecasting and Social Change 95 (2015) 96ndash108

[7] Pete Burnap and Matthew L Williams 2016 Us and them identifyingcyber hate on Twitter across multiple protected characteristics EPJData Science 5 1 (23 Mar 2016) 11 httpsdoiorg101140epjdss13688-016-0072-6

[8] Despoina Chatzakou Nicolas Kourtellis Jeremy Blackburn EmilianoDe Cristofaro Gianluca Stringhini and Athena Vakali 2017 Hateis not binary Studying abusive behavior of gamergate on twitterProceedings of the 28th ACM conference on hypertext and social media65ndash74

[9] Despoina Chatzakou Nicolas Kourtellis Jeremy Blackburn EmilianoDe Cristofaro Gianluca Stringhini and Athena Vakali 2017 Meanbirds Detecting aggression and bullying on twitter Proceedings of the2017 ACM on web science conference 13ndash22

[10] Thomas Davidson Dana Warmsley Michael Macy and Ingmar Weber2017 Automated hate speech detection and the problem of offensivelanguage Proceedings of ICWSM (2017)

[11] Karthik Dinakar Birago Jones Catherine Havasi Henry Liebermanand Rosalind Picard 2012 Common sense reasoning for detectionprevention and mitigation of cyberbullying ACM Transactions onInteractive Intelligent Systems (TiiS) 2 3 (2012) 18

[12] Jacob Eisenstein Amr Ahmed and Eric P Xing 2011 Sparse additivegenerative models of text Proceedings of the 28th International Confer-ence on International Conference on Machine Learning 1041ndash1048

[13] Mai ElSherief Vivek Kulkarni Dana Nguyen William Yang Wangand Elizabeth Belding 2018 Hate Lingo A Target-based LinguisticAnalysis of Hate Speech in Social Media ICWSM (2018)

[14] Mai ElSherief Shirin Nilizadeh Dana Nguyen Giovanni Vigna andElizabeth Belding 2018 Peer to Peer Hate Hate Speech Instigatorsand Their Targets ICWSM (2018)

[15] Facebook 2016 Controversial Harmful and Hateful Speech on Face-book httpsgooglTWAHdr

[16] Antigoni-Maria Founta Constantinos Djouvas Despoina ChatzakouIlias Leontiadis Jeremy Blackburn Gianluca Stringhini Athena VakaliMichael Sirivianos and Nicolas Kourtellis 2018 Large Scale Crowd-sourcing and Characterization of Twitter Abusive Behavior Interna-tional Conference on Social Web and Media Log (2018)

[17] Howard Giles 2005 Intergroup communication Multiple perspectivesVol 2 Peter Lang

[18] Njagi Dennis Gitari Zhang Zuping Hanyurwimfura Damien andJun Long 2015 A lexicon-based approach for hate speech detectionInternational Journal of Multimedia and Ubiquitous Engineering 10 4(2015) 215ndash230

[19] Srijan Kumar William L Hamilton Jure Leskovec and Dan Jurafsky2018 Community interaction and conflict on the web Proceedings ofthe 2018 World Wide Web Conference on World Wide Web 933ndash943

[20] Irene Kwok and YuzhouWang 2013 Locate the Hate Detecting Tweetsagainst Blacks AAAI

[21] Altaf Mahmud Kazi Zubair Ahmed and Mumit Khan 2008 Detectingflames and insults in text (2008)

[22] Yashar Mehdad and Joel Tetreault 2016 Do Characters Abuse MoreThan Words Proceedings of the 17th Annual Meeting of the SpecialInterest Group on Discourse and Dialogue 299ndash303

[23] Fred Morstatter Juumlrgen Pfeffer Huan Liu and Kathleen M Carley2013 Is the Sample Good Enough Comparing Data from TwitterrsquosStreaming API with Twitterrsquos Firehose ICWSM

[24] Chikashi Nobata Joel Tetreault Achint Thomas Yashar Mehdad andYi Chang 2016 Abusive language detection in online user contentProceedings of the 25th international conference on world wide web145ndash153

[25] Alexandra Olteanu Carlos Castillo Jeremy Boy and Kush R Varshney2018 The Effect of Extremist Violence on Hateful Speech Online arXivpreprint arXiv180405704 (2018)

[26] James W Pennebaker Ryan L Boyd Kayla Jordan and Kate Black-burn 2015 The development and psychometric properties of LIWC2015Technical Report

[27] Koustav Rudra Siddhartha Banerjee Niloy Ganguly Pawan GoyalMuhammad Imran and Prasenjit Mitra 2016 Summarizing situationaltweets in crisis scenario Proceedings of the 27th ACM Conference onHypertext and Social Media 137ndash147

[28] SafeHomeorg 2017 Hate on Social Media httpswwwsafehomeorgresourceshate-on-social-media

[29] Muzafer Sherif 2015 Group conflict and co-operation Their socialpsychology Psychology Press

[30] Leandro Arauacutejo Silva Mainack Mondal Denzil Correa Fabriacutecio Ben-evenuto and Ingmar Weber 2016 Analyzing the Targets of Hate inOnline Social Media ICWSM 687ndash690

[31] Henri Tajfel and John C Turner 1979 An integrative theory of in-tergroup conflict The social psychology of intergroup relations 33 47(1979) 74

[32] Duyu Tang Bing Qin Xiaocheng Feng and Ting Liu 2016 EffectiveLSTMs for target-dependent sentiment classification InternationalConference on Computational Linguistics (2016) 3298ndash3307

[33] Duyu Tang Bing Qin Xiaocheng Feng and Ting Liu 2016 Target-Dependent Sentiment Classification with Long Short Term Mem-ory Proceedings of COLING 2016 the 26th International Conferenceon Computational Linguistics Technical Papers abs151201100 (2016)3298acircĂŞ3307

[34] Ginka Toegel and Jean-Louis Barsoux 2012 How to become a betterleader MIT Sloan Management Review 53 3 (2012) 51ndash60

[35] Zeynep Tufekci 2014 Big Questions for Social Media Big Data Repre-sentativeness Validity and Other Methodological Pitfalls ICWSM 14(2014) 505ndash514

[36] Twitter 2016 Hateful Conduct Policy httpssupporttwittercomarticles20175050

[37] Cynthia Van Hee Els Lefever Ben Verhoeven Julie Mennes BartDesmet Guy De Pauw Walter Daelemans and Veacuteronique Hoste 2015Detection and fine-grained classification of cyberbullying events In-ternational Conference Recent Advances in Natural Language Processing(RANLP) 672ndash680

[38] Zeerak Waseem and Dirk Hovy 2016 Hateful Symbols or HatefulPeople Predictive Features for Hate Speech Detection on TwitterProceedings of the NAACL Student Research Workshop 88ndash93 httpwwwaclweborganthologyN16-2013

[39] Michael Wiegand Josef Ruppenhofer Anna Schmidt and ClaytonGreenberg 2018 Inducing a Lexicon of Abusive Wordsndasha Feature-Based Approach Proceedings of the 2018 Conference of the North Amer-ican Chapter of the Association for Computational Linguistics HumanLanguage Technologies Volume 1 (Long Papers) 1 1046ndash1056

IUI Workshopsrsquo19 March 20 2019 Los Angeles USA Khosla et al

0 100 200 300 400

0

200

400

600

800

1000HS(Sbarca Tmadrid)

0 100 200 300 400

0

200

400

600

800

1000HS(Smadrid Tbarca)

Figure 2 Number of hate tweets exchanged between Real Madrid FC and FC Barcelona in 2017-18 season Vertical linesdenote a sample of events during the season Green corresponds to el-classico matches black and orange lines represent

matches which saw smallest and largest amount of hate respectively from the rival community

0 100 200 300 400

0

1

2

3

4

5middot10minus2

ratio

HS(Sall Tmadrid)

0 100 200 300 400

0

02

04

06

08

1

middot10minus2

ratio

HS(Sbarca Tmadrid)

Figure 3 Ratio of lsquohate speech towards Real Madrid FCrsquo to lsquoGeneral Mentions of Real Madrid FCrsquo during the 2017-18 season

Impact of offline events on hate speech onlineFollowing the inference from the previous section we nowtry to quantify the impact an event had on online hate speechWe posit that an event (a football match in our case) has highimpact on hate speech if it results in an increased relativeamount of hate speech against non-event days To analyzethe impact of events on online hate exchange between thetwo communities of interest we plot a time-series of theratio of HS(SsTt) to general tweets mentioning target com-munity t (Figure 3 shows the time-series for Tmadrid) Wequantify their effect by treating them as interventions on ob-served time series Following [25] we use Brodersen et alrsquostechnique [5] for causal inference on time-series to quantifythe impact of football events on hate speech The behaviorof observed time-series (treatment) after a football match iscompared with a counterfactual time series (control) Sincewe do not observe the control time-series we model it fromobserved time-series (in different time ranges) that correlatewith the treatment series but were not affected by the eventFinally we use this setup to model the counterfactual of thetreated time series using difference-in-differences approach

A Observed Time-SeriesWe define our treatment series witha timespan of two days before the match as pre-treatmentperiod and two days after the match as post-treatment periodB Synthetic Control Group creationWe then identify possiblecontrol groups as time-series that occur in history duringsame days of the week as the treatment series with no eventtaking place during this time interval We rank all controlgroups based on their similarity to the treatment group usingWilcoxon signed rank test and select the top 2 ranked timeseries for creating our synthetic control time seriesC Impact Estimation We finally use the difference betweenobserved post treatment time series and the synthetic con-trol time series to calculate the impact of event The relativeincrease in online hate speech during an event is given by

relef f ect = 100 lowastsumtk minus cksumck

(1)

where tk is the value of the treatment time series at time k and ck that of the control time series

Do all events contribute to the hate speech equally Our re-sults show that outcome of matches seems to affect the hate

Understanding Community Rivalry on Social Media IUI Workshopsrsquo19 March 20 2019 Los Angeles USA

HatefulUsers(HU)

General Hate( of total tweets)

Event Overlap( of total events) Followers

Mean() Users whopost gt 10 Mean() Users who

post in gt 20 Mean Userswith lt 100

Users withgt 10 000

T madridSall 987 4373 1176 008 257058 2166 246

Sbarca 1009 4592 1145 001 206283 1829 264T barca

Sall 910 3617 1480 005 300074 3700 370Smadrid 936 3940 1384 000 106966 3045 160

Table 2 Characterizing hateful users

Figure 4 Example of impact estimation withcounterfactual predictions for the event Champions

League 2018 final between Real Madrid FC and LiverpoolFC Top Black is the observed series beforeafter the event

blue (dashed) is the counterfactual

exchange We find that losses and draws trigger greater hatespeech than wins (p lt 0001) We also observe that HomeLosses andDraws trigger greater hate in comparison to AwayLosses andDraws (p lt 0001) In contrast AwayWins triggergreater hate than Home Wins (HU(Sall Tmadrid) p lt 001HU(Sbarca Tmadrid) p lt 0001) particularly from therival community Moreover group games trigger less hatein comparison to championship games (HU(Sall Tmadrid)p = 005 HU(Sbarca Tmadrid) p lt 005) Matches whichlead to elimination (HU(Sall Tmadrid) p lt 0001 HU(SallTmadrid) p lt 005) trigger greater hate Matches playedagainst the rival community tend to bring more hate fromthe rival community (p lt 0001) More generally we findthat rival community posts disproportionately higher hatefor matches whose results directly impact them A similartrend is observed for t = barca (omitted for brevity)

Characterizing hateful usersIn this section we analyze HU(Ss Tt) Users who post hate-ful content against the target community of interest

Do they post hate speech in general We investigate if thehateful users in our domain use hate speech in their generaltweets as well We extract their 3200 most recent tweetsand classify them using our hate speech detection model(explained in the earlier sections) after excluding tweets thatmentioned entities related to Real Madrid FC or FC BarcelonaWe observe that users who post hate in our domain alsopropagate significant hate in general with around 10 oftheir general tweets being hateful (Table 2) Our results showthat HU(S rival) post more hate in general as against HU(Sall) with a higher percentage of HU(S barca T madrid)crossing the 10 mark as compared to HU(S all T madrid)(4592 vs 4373) A similar trend is observed for Tbarca

Do they post hate in multiple events We then analyze theuser overlap across different events to see if there is a com-mon set of users who post hate during multiple events Anyuser who posts in a 24hr interval after the event is assumedto have posted because of that event We find that around90HUs write hate tweets for only 1minus2 of football matchesthroughout the year Whereas less than 01 HUs post hatein more than 20 of the total football matches in 2017-18 sea-son (Table 2) Members of the rival community HU(Srival)on average show lower event overlap compared to HU(Sall)This is consistent with the findings in the Analysis sectionas the rival community is only interested in events whichimpact them directly

Are they popular Next we check if the users who writehate tweets enjoy popularity on Twitter We use numberof followers as a metric to do the popularity analysis Asshown in Table 2 target-specific hate in our domain is postedby common masses (less than 100 followers) whereas thepopular users (more than 10000 followers) seldom (lt 4)participate in this phenomenon Popular members of Real

IUI Workshopsrsquo19 March 20 2019 Los Angeles USA Khosla et al

Madrid FC community seem to avoid hate speech against FCBarcelona (16)

What are their key personality traits To study the key char-acteristics of the personalities of HUs in our domain we usethe Twitter REST API to fetch the most recent 3200 tweetsfor each account We exclude retweets as they might not re-flect authorrsquos point of view and use IBM Watson PersonalityInsights API1 for this analysis It outputs a normalized per-centile score for the characteristic We study the results of theBig Five personality model the most widely used model forgenerally describing how a person engages with the worldThe model includes five primary dimensions AgreeablenessConscientiousness Extraversion Neuroticism and Open-ness [2]Figure 5 shows the distribution of scores of the Big Five

personality traits for Tmadrid We find that HS(Sall) andHS(Sbarca) have more similar personalities to each otherthan general mentions Both HS(Sall) and HS(Sbarca) ex-hibit lower Agreeableness than general mentions Prior work[34] associates lower Agreeableness scores with suspiciousand antagonistic behaviors Our results indicate that HS(Sall)and HS(Sbarca) are more self-focused contrary cautious ofothers and lack empathy For Conscientiousness HS(Sall)and HS(Sbarca) generally have lower scores than generalmentions Our results suggest that these users are laid backless goal-oriented and tend to disregard rules Low Extraver-sion scores for both HS(Sall) and HS(Sbarca) show that theyare less sociable less assertive and more within themselvesHS(Sall) and HS(Sbarca) have slightly higher but statisti-cally significant scores for Neuroticism which indicates thatthey are more susceptible to stress and are more likely to ex-perience anxiety jealousy and anger However for Opennessthe distributions for HS(Sall) and HS(Sbarca) are close togeneral mentions (with median of approximately 019) Weobserve a similar trend for Tbarca but omit here for brevity

Characterizing hateful tweetsAre these hate tweets popular We next investigate the popu-larity of target-specific hate tweets in our domain We usethe retweet-count of tweets to judge their popularity We ob-serve that for target community FC Barcelona (Tbarca) hate-ful tweets from Real Madrid ie HS(Smadrid Tbarca) areretweeted less than general hateful tweets HS(Sall Tbarca)(with mean = 019 and 053 respectively) which in turn aresubstantially less popular than non-hateful tweets (mean =547)

Content Characteristics We use SAGE [12] to analyze salientwords that characterize different types of tweets SAGE at-tempts to find salient terms in a text (child) with respect to

1httpswwwibmcomwatsonservicespersonality-insights

Exp Top 10 Salient Words

(i) fk bch fked fking adore cnt yer kidcheating idiot

(ii) fk fked fking bch cnt bullst adoreshty sht cheating

(iii)granada rampant messi bartomeudimiteya

valverde forcabarca viscabarca yermadridiots penaldo

Table 3 Top 10 salient words learned by SAGE for differentexperiments with Tmadrid Note the presence of barca

specific keywords (bold) in (iii)

some base content (base) It creates clean topic models bytaking into account the additive effects and combines multi-ple generative facets like topic and perspective distributionof words In this analysis we conduct three experiments (i)child = HS(Sall) base = tweets which mention the targetcommunity (ii) child = HS(Srival)base = tweets which men-tion the target community and (iii) child = HS(Srival) base= HS(Sall) We look at the top 10 salient words learned forthe above-mentioned experiments (Table 3)

As a whole both (i) and (ii) contain similar salient wordsThese words are mostly cuss words which is to be expectedThe top salient words in (iii) contain mentions of the entitiesof the source community On closer look we find that thesetweets try to demean the target community (eg playersmanagers or ideology etc) in an attempt to glorify the sourcecommunity For example this hate tweet by a Real Madrid FCsupporter against FC Barcelona king dem ronaldo king demleft and right salute d kingi want to take this opportunity andsay fk all barcelona fans fcbarcelona _es tries to glorifyRonaldo (an ex Real Madrid FC player) by calling him a KingSuch a pattern of comparison is not visible in HS(Sall) whichmostly focuses on the negatives of the target communityFor example this hate tweet against FC Barcelona from auser who is not a Real Madrid FC community member DearFCBarcelona please take your sht (Bellerin) back Please

Psycholinguistic Analysis We use LIWC2015 [26] for a fullpsycholinguistic analysis We look at the following dimen-sions summary scores personal pronouns and negativeemotions2 In Figure 6 we can see that tweets with general(non-hate) mentions (NHM) of Real Madrid FC differ signifi-cantly from hateful tweets Summary scores suggest that gen-eral tweets display higher values of tone than HS(Tmadrid)suggesting that targeted hate speech is more hostile HS(Tmadrid) contains higher number of pronouns and is angrierthan general tweets Also HS(Tmadrid) is more informal andexpectedly contain more swear words It contains shortersentences and uses more dictionary words on average as

2LIWC2015 language manual [26] provides a detailed description for thesedimensions

Understanding Community Rivalry on Social Media IUI Workshopsrsquo19 March 20 2019 Los Angeles USA

Figure 5 Distribution of scores for the Big Five personality traits for users who posted tweets that mention Real Madrid FCGeneral Mentions are tweets that mention the target community or its associated entities

Tone Analytic Clout WPS Dic0

20

40

60

80

100

Score

HS(Sall) HS(Sbarca) NHM

(a) Summary

i we they you shehe0

05

1

15

2

Score

HS(Sall) HS(Sbarca) NHM

(b) Personal Pronouns

anger sad anx0

2

4

6

Score

HS(Sall) HS(Sbarca) NHM

(c) Negative Emotions

Figure 6 Psycholinguistic Analysis of tweets whichmention Real Madrid FC (Tmadrid)

against the non-hate tweets mentioning Real Madrid FC Asimilar trend is observed for Tbarca (omitted for brevity)

7 DISCUSSION AND CONCLUSIONSIn this work we provide a novel view of hate exchangebetween different communities in the football domain Wedesign a two-step model to detect target-specific hate speechUsing causal inference methodologies we are able to mea-sure the effect of external events on hate speech on social

media We show how rival communities post disproportion-ately high amount of hate during events which have a directimpact on their teamrsquos interests We find that hateful usersin our domain also post hate speech in general They donot post hate in multiple events and do not enjoy generouspopularity on social media We show that their personalitycharacteristics are significantly different from general userswho post about the target community Our analysis showsthat hate tweets from rival community members differ intheir theme from general hate tweets towards the targetcommunity They try to glorify their teamrsquos players playingstyle or ideology while demeaning the target communityHowever the psycholinguistic analysis of the hateful tweetssuggests that content from rival community does not differfrom general tweets in terms of the emotional content tonepronoun usage or swear wordsNonetheless our analysis has limitations Recent studies

by Tufekci [35] and Morstatter et al [23] have discussed thesample quality of the Twitter API Since our analysis relies onkeyword-based methods for retrieval of explicit hate speechwe cannot claim to have captured a complete representationof the hate exchange on Twitter However our main objectivewas to characterize hateful users and tweets in the sportsdomain with high precision and we believe that our carefulfiltering and classification models were able to do so

REFERENCES[1] Pinkesh Badjatiya Shashank Gupta Manish Gupta and Vasudeva

Varma 2017 Deep learning for hate speech detection in tweets Pro-ceedings of the 26th International Conference on World Wide Web Com-panion 759ndash760

IUI Workshopsrsquo19 March 20 2019 Los Angeles USA Khosla et al

[2] Murray R Barrick and Michael K Mount 1991 The big five personalitydimensions and job performance ameta-analysis Personnel psychology44 1 (1991) 1ndash26

[3] Vaacuteclav Belaacutek Samantha Lam and Conor Hayes 2012 Cross-Community Influence in Discussion Fora ICWSM 12 (2012) 34ndash41

[4] Susan Benesch 2014 Countering dangerous speech to prevent massviolence during KenyaacircĂŹs 2013 elections Final Report (2014) 1ndash26

[5] Kay H Brodersen Fabian Gallusser Jim Koehler Nicolas Remy andSteven L Scott 2015 Inferring causal impact using Bayesian structuraltime-series models Annals of Applied Statistics 9 (2015) 247ndash274

[6] Pete Burnap Omer F Rana Nick Avis Matthew Williams WilliamHousley Adam Edwards Jeffrey Morgan and Luke Sloan 2015 Detect-ing tension in online communities with computational Twitter analysisTechnological Forecasting and Social Change 95 (2015) 96ndash108

[7] Pete Burnap and Matthew L Williams 2016 Us and them identifyingcyber hate on Twitter across multiple protected characteristics EPJData Science 5 1 (23 Mar 2016) 11 httpsdoiorg101140epjdss13688-016-0072-6

[8] Despoina Chatzakou Nicolas Kourtellis Jeremy Blackburn EmilianoDe Cristofaro Gianluca Stringhini and Athena Vakali 2017 Hateis not binary Studying abusive behavior of gamergate on twitterProceedings of the 28th ACM conference on hypertext and social media65ndash74

[9] Despoina Chatzakou Nicolas Kourtellis Jeremy Blackburn EmilianoDe Cristofaro Gianluca Stringhini and Athena Vakali 2017 Meanbirds Detecting aggression and bullying on twitter Proceedings of the2017 ACM on web science conference 13ndash22

[10] Thomas Davidson Dana Warmsley Michael Macy and Ingmar Weber2017 Automated hate speech detection and the problem of offensivelanguage Proceedings of ICWSM (2017)

[11] Karthik Dinakar Birago Jones Catherine Havasi Henry Liebermanand Rosalind Picard 2012 Common sense reasoning for detectionprevention and mitigation of cyberbullying ACM Transactions onInteractive Intelligent Systems (TiiS) 2 3 (2012) 18

[12] Jacob Eisenstein Amr Ahmed and Eric P Xing 2011 Sparse additivegenerative models of text Proceedings of the 28th International Confer-ence on International Conference on Machine Learning 1041ndash1048

[13] Mai ElSherief Vivek Kulkarni Dana Nguyen William Yang Wangand Elizabeth Belding 2018 Hate Lingo A Target-based LinguisticAnalysis of Hate Speech in Social Media ICWSM (2018)

[14] Mai ElSherief Shirin Nilizadeh Dana Nguyen Giovanni Vigna andElizabeth Belding 2018 Peer to Peer Hate Hate Speech Instigatorsand Their Targets ICWSM (2018)

[15] Facebook 2016 Controversial Harmful and Hateful Speech on Face-book httpsgooglTWAHdr

[16] Antigoni-Maria Founta Constantinos Djouvas Despoina ChatzakouIlias Leontiadis Jeremy Blackburn Gianluca Stringhini Athena VakaliMichael Sirivianos and Nicolas Kourtellis 2018 Large Scale Crowd-sourcing and Characterization of Twitter Abusive Behavior Interna-tional Conference on Social Web and Media Log (2018)

[17] Howard Giles 2005 Intergroup communication Multiple perspectivesVol 2 Peter Lang

[18] Njagi Dennis Gitari Zhang Zuping Hanyurwimfura Damien andJun Long 2015 A lexicon-based approach for hate speech detectionInternational Journal of Multimedia and Ubiquitous Engineering 10 4(2015) 215ndash230

[19] Srijan Kumar William L Hamilton Jure Leskovec and Dan Jurafsky2018 Community interaction and conflict on the web Proceedings ofthe 2018 World Wide Web Conference on World Wide Web 933ndash943

[20] Irene Kwok and YuzhouWang 2013 Locate the Hate Detecting Tweetsagainst Blacks AAAI

[21] Altaf Mahmud Kazi Zubair Ahmed and Mumit Khan 2008 Detectingflames and insults in text (2008)

[22] Yashar Mehdad and Joel Tetreault 2016 Do Characters Abuse MoreThan Words Proceedings of the 17th Annual Meeting of the SpecialInterest Group on Discourse and Dialogue 299ndash303

[23] Fred Morstatter Juumlrgen Pfeffer Huan Liu and Kathleen M Carley2013 Is the Sample Good Enough Comparing Data from TwitterrsquosStreaming API with Twitterrsquos Firehose ICWSM

[24] Chikashi Nobata Joel Tetreault Achint Thomas Yashar Mehdad andYi Chang 2016 Abusive language detection in online user contentProceedings of the 25th international conference on world wide web145ndash153

[25] Alexandra Olteanu Carlos Castillo Jeremy Boy and Kush R Varshney2018 The Effect of Extremist Violence on Hateful Speech Online arXivpreprint arXiv180405704 (2018)

[26] James W Pennebaker Ryan L Boyd Kayla Jordan and Kate Black-burn 2015 The development and psychometric properties of LIWC2015Technical Report

[27] Koustav Rudra Siddhartha Banerjee Niloy Ganguly Pawan GoyalMuhammad Imran and Prasenjit Mitra 2016 Summarizing situationaltweets in crisis scenario Proceedings of the 27th ACM Conference onHypertext and Social Media 137ndash147

[28] SafeHomeorg 2017 Hate on Social Media httpswwwsafehomeorgresourceshate-on-social-media

[29] Muzafer Sherif 2015 Group conflict and co-operation Their socialpsychology Psychology Press

[30] Leandro Arauacutejo Silva Mainack Mondal Denzil Correa Fabriacutecio Ben-evenuto and Ingmar Weber 2016 Analyzing the Targets of Hate inOnline Social Media ICWSM 687ndash690

[31] Henri Tajfel and John C Turner 1979 An integrative theory of in-tergroup conflict The social psychology of intergroup relations 33 47(1979) 74

[32] Duyu Tang Bing Qin Xiaocheng Feng and Ting Liu 2016 EffectiveLSTMs for target-dependent sentiment classification InternationalConference on Computational Linguistics (2016) 3298ndash3307

[33] Duyu Tang Bing Qin Xiaocheng Feng and Ting Liu 2016 Target-Dependent Sentiment Classification with Long Short Term Mem-ory Proceedings of COLING 2016 the 26th International Conferenceon Computational Linguistics Technical Papers abs151201100 (2016)3298acircĂŞ3307

[34] Ginka Toegel and Jean-Louis Barsoux 2012 How to become a betterleader MIT Sloan Management Review 53 3 (2012) 51ndash60

[35] Zeynep Tufekci 2014 Big Questions for Social Media Big Data Repre-sentativeness Validity and Other Methodological Pitfalls ICWSM 14(2014) 505ndash514

[36] Twitter 2016 Hateful Conduct Policy httpssupporttwittercomarticles20175050

[37] Cynthia Van Hee Els Lefever Ben Verhoeven Julie Mennes BartDesmet Guy De Pauw Walter Daelemans and Veacuteronique Hoste 2015Detection and fine-grained classification of cyberbullying events In-ternational Conference Recent Advances in Natural Language Processing(RANLP) 672ndash680

[38] Zeerak Waseem and Dirk Hovy 2016 Hateful Symbols or HatefulPeople Predictive Features for Hate Speech Detection on TwitterProceedings of the NAACL Student Research Workshop 88ndash93 httpwwwaclweborganthologyN16-2013

[39] Michael Wiegand Josef Ruppenhofer Anna Schmidt and ClaytonGreenberg 2018 Inducing a Lexicon of Abusive Wordsndasha Feature-Based Approach Proceedings of the 2018 Conference of the North Amer-ican Chapter of the Association for Computational Linguistics HumanLanguage Technologies Volume 1 (Long Papers) 1 1046ndash1056

Understanding Community Rivalry on Social Media IUI Workshopsrsquo19 March 20 2019 Los Angeles USA

HatefulUsers(HU)

General Hate( of total tweets)

Event Overlap( of total events) Followers

Mean() Users whopost gt 10 Mean() Users who

post in gt 20 Mean Userswith lt 100

Users withgt 10 000

T madridSall 987 4373 1176 008 257058 2166 246

Sbarca 1009 4592 1145 001 206283 1829 264T barca

Sall 910 3617 1480 005 300074 3700 370Smadrid 936 3940 1384 000 106966 3045 160

Table 2 Characterizing hateful users

Figure 4 Example of impact estimation withcounterfactual predictions for the event Champions

League 2018 final between Real Madrid FC and LiverpoolFC Top Black is the observed series beforeafter the event

blue (dashed) is the counterfactual

exchange We find that losses and draws trigger greater hatespeech than wins (p lt 0001) We also observe that HomeLosses andDraws trigger greater hate in comparison to AwayLosses andDraws (p lt 0001) In contrast AwayWins triggergreater hate than Home Wins (HU(Sall Tmadrid) p lt 001HU(Sbarca Tmadrid) p lt 0001) particularly from therival community Moreover group games trigger less hatein comparison to championship games (HU(Sall Tmadrid)p = 005 HU(Sbarca Tmadrid) p lt 005) Matches whichlead to elimination (HU(Sall Tmadrid) p lt 0001 HU(SallTmadrid) p lt 005) trigger greater hate Matches playedagainst the rival community tend to bring more hate fromthe rival community (p lt 0001) More generally we findthat rival community posts disproportionately higher hatefor matches whose results directly impact them A similartrend is observed for t = barca (omitted for brevity)

Characterizing hateful usersIn this section we analyze HU(Ss Tt) Users who post hate-ful content against the target community of interest

Do they post hate speech in general We investigate if thehateful users in our domain use hate speech in their generaltweets as well We extract their 3200 most recent tweetsand classify them using our hate speech detection model(explained in the earlier sections) after excluding tweets thatmentioned entities related to Real Madrid FC or FC BarcelonaWe observe that users who post hate in our domain alsopropagate significant hate in general with around 10 oftheir general tweets being hateful (Table 2) Our results showthat HU(S rival) post more hate in general as against HU(Sall) with a higher percentage of HU(S barca T madrid)crossing the 10 mark as compared to HU(S all T madrid)(4592 vs 4373) A similar trend is observed for Tbarca

Do they post hate in multiple events We then analyze theuser overlap across different events to see if there is a com-mon set of users who post hate during multiple events Anyuser who posts in a 24hr interval after the event is assumedto have posted because of that event We find that around90HUs write hate tweets for only 1minus2 of football matchesthroughout the year Whereas less than 01 HUs post hatein more than 20 of the total football matches in 2017-18 sea-son (Table 2) Members of the rival community HU(Srival)on average show lower event overlap compared to HU(Sall)This is consistent with the findings in the Analysis sectionas the rival community is only interested in events whichimpact them directly

Are they popular Next we check if the users who writehate tweets enjoy popularity on Twitter We use numberof followers as a metric to do the popularity analysis Asshown in Table 2 target-specific hate in our domain is postedby common masses (less than 100 followers) whereas thepopular users (more than 10000 followers) seldom (lt 4)participate in this phenomenon Popular members of Real

IUI Workshopsrsquo19 March 20 2019 Los Angeles USA Khosla et al

Madrid FC community seem to avoid hate speech against FCBarcelona (16)

What are their key personality traits To study the key char-acteristics of the personalities of HUs in our domain we usethe Twitter REST API to fetch the most recent 3200 tweetsfor each account We exclude retweets as they might not re-flect authorrsquos point of view and use IBM Watson PersonalityInsights API1 for this analysis It outputs a normalized per-centile score for the characteristic We study the results of theBig Five personality model the most widely used model forgenerally describing how a person engages with the worldThe model includes five primary dimensions AgreeablenessConscientiousness Extraversion Neuroticism and Open-ness [2]Figure 5 shows the distribution of scores of the Big Five

personality traits for Tmadrid We find that HS(Sall) andHS(Sbarca) have more similar personalities to each otherthan general mentions Both HS(Sall) and HS(Sbarca) ex-hibit lower Agreeableness than general mentions Prior work[34] associates lower Agreeableness scores with suspiciousand antagonistic behaviors Our results indicate that HS(Sall)and HS(Sbarca) are more self-focused contrary cautious ofothers and lack empathy For Conscientiousness HS(Sall)and HS(Sbarca) generally have lower scores than generalmentions Our results suggest that these users are laid backless goal-oriented and tend to disregard rules Low Extraver-sion scores for both HS(Sall) and HS(Sbarca) show that theyare less sociable less assertive and more within themselvesHS(Sall) and HS(Sbarca) have slightly higher but statisti-cally significant scores for Neuroticism which indicates thatthey are more susceptible to stress and are more likely to ex-perience anxiety jealousy and anger However for Opennessthe distributions for HS(Sall) and HS(Sbarca) are close togeneral mentions (with median of approximately 019) Weobserve a similar trend for Tbarca but omit here for brevity

Characterizing hateful tweetsAre these hate tweets popular We next investigate the popu-larity of target-specific hate tweets in our domain We usethe retweet-count of tweets to judge their popularity We ob-serve that for target community FC Barcelona (Tbarca) hate-ful tweets from Real Madrid ie HS(Smadrid Tbarca) areretweeted less than general hateful tweets HS(Sall Tbarca)(with mean = 019 and 053 respectively) which in turn aresubstantially less popular than non-hateful tweets (mean =547)

Content Characteristics We use SAGE [12] to analyze salientwords that characterize different types of tweets SAGE at-tempts to find salient terms in a text (child) with respect to

1httpswwwibmcomwatsonservicespersonality-insights

Exp Top 10 Salient Words

(i) fk bch fked fking adore cnt yer kidcheating idiot

(ii) fk fked fking bch cnt bullst adoreshty sht cheating

(iii)granada rampant messi bartomeudimiteya

valverde forcabarca viscabarca yermadridiots penaldo

Table 3 Top 10 salient words learned by SAGE for differentexperiments with Tmadrid Note the presence of barca

specific keywords (bold) in (iii)

some base content (base) It creates clean topic models bytaking into account the additive effects and combines multi-ple generative facets like topic and perspective distributionof words In this analysis we conduct three experiments (i)child = HS(Sall) base = tweets which mention the targetcommunity (ii) child = HS(Srival)base = tweets which men-tion the target community and (iii) child = HS(Srival) base= HS(Sall) We look at the top 10 salient words learned forthe above-mentioned experiments (Table 3)

As a whole both (i) and (ii) contain similar salient wordsThese words are mostly cuss words which is to be expectedThe top salient words in (iii) contain mentions of the entitiesof the source community On closer look we find that thesetweets try to demean the target community (eg playersmanagers or ideology etc) in an attempt to glorify the sourcecommunity For example this hate tweet by a Real Madrid FCsupporter against FC Barcelona king dem ronaldo king demleft and right salute d kingi want to take this opportunity andsay fk all barcelona fans fcbarcelona _es tries to glorifyRonaldo (an ex Real Madrid FC player) by calling him a KingSuch a pattern of comparison is not visible in HS(Sall) whichmostly focuses on the negatives of the target communityFor example this hate tweet against FC Barcelona from auser who is not a Real Madrid FC community member DearFCBarcelona please take your sht (Bellerin) back Please

Psycholinguistic Analysis We use LIWC2015 [26] for a fullpsycholinguistic analysis We look at the following dimen-sions summary scores personal pronouns and negativeemotions2 In Figure 6 we can see that tweets with general(non-hate) mentions (NHM) of Real Madrid FC differ signifi-cantly from hateful tweets Summary scores suggest that gen-eral tweets display higher values of tone than HS(Tmadrid)suggesting that targeted hate speech is more hostile HS(Tmadrid) contains higher number of pronouns and is angrierthan general tweets Also HS(Tmadrid) is more informal andexpectedly contain more swear words It contains shortersentences and uses more dictionary words on average as

2LIWC2015 language manual [26] provides a detailed description for thesedimensions

Understanding Community Rivalry on Social Media IUI Workshopsrsquo19 March 20 2019 Los Angeles USA

Figure 5 Distribution of scores for the Big Five personality traits for users who posted tweets that mention Real Madrid FCGeneral Mentions are tweets that mention the target community or its associated entities

Tone Analytic Clout WPS Dic0

20

40

60

80

100

Score

HS(Sall) HS(Sbarca) NHM

(a) Summary

i we they you shehe0

05

1

15

2

Score

HS(Sall) HS(Sbarca) NHM

(b) Personal Pronouns

anger sad anx0

2

4

6

Score

HS(Sall) HS(Sbarca) NHM

(c) Negative Emotions

Figure 6 Psycholinguistic Analysis of tweets whichmention Real Madrid FC (Tmadrid)

against the non-hate tweets mentioning Real Madrid FC Asimilar trend is observed for Tbarca (omitted for brevity)

7 DISCUSSION AND CONCLUSIONSIn this work we provide a novel view of hate exchangebetween different communities in the football domain Wedesign a two-step model to detect target-specific hate speechUsing causal inference methodologies we are able to mea-sure the effect of external events on hate speech on social

media We show how rival communities post disproportion-ately high amount of hate during events which have a directimpact on their teamrsquos interests We find that hateful usersin our domain also post hate speech in general They donot post hate in multiple events and do not enjoy generouspopularity on social media We show that their personalitycharacteristics are significantly different from general userswho post about the target community Our analysis showsthat hate tweets from rival community members differ intheir theme from general hate tweets towards the targetcommunity They try to glorify their teamrsquos players playingstyle or ideology while demeaning the target communityHowever the psycholinguistic analysis of the hateful tweetssuggests that content from rival community does not differfrom general tweets in terms of the emotional content tonepronoun usage or swear wordsNonetheless our analysis has limitations Recent studies

by Tufekci [35] and Morstatter et al [23] have discussed thesample quality of the Twitter API Since our analysis relies onkeyword-based methods for retrieval of explicit hate speechwe cannot claim to have captured a complete representationof the hate exchange on Twitter However our main objectivewas to characterize hateful users and tweets in the sportsdomain with high precision and we believe that our carefulfiltering and classification models were able to do so

REFERENCES[1] Pinkesh Badjatiya Shashank Gupta Manish Gupta and Vasudeva

Varma 2017 Deep learning for hate speech detection in tweets Pro-ceedings of the 26th International Conference on World Wide Web Com-panion 759ndash760

IUI Workshopsrsquo19 March 20 2019 Los Angeles USA Khosla et al

[2] Murray R Barrick and Michael K Mount 1991 The big five personalitydimensions and job performance ameta-analysis Personnel psychology44 1 (1991) 1ndash26

[3] Vaacuteclav Belaacutek Samantha Lam and Conor Hayes 2012 Cross-Community Influence in Discussion Fora ICWSM 12 (2012) 34ndash41

[4] Susan Benesch 2014 Countering dangerous speech to prevent massviolence during KenyaacircĂŹs 2013 elections Final Report (2014) 1ndash26

[5] Kay H Brodersen Fabian Gallusser Jim Koehler Nicolas Remy andSteven L Scott 2015 Inferring causal impact using Bayesian structuraltime-series models Annals of Applied Statistics 9 (2015) 247ndash274

[6] Pete Burnap Omer F Rana Nick Avis Matthew Williams WilliamHousley Adam Edwards Jeffrey Morgan and Luke Sloan 2015 Detect-ing tension in online communities with computational Twitter analysisTechnological Forecasting and Social Change 95 (2015) 96ndash108

[7] Pete Burnap and Matthew L Williams 2016 Us and them identifyingcyber hate on Twitter across multiple protected characteristics EPJData Science 5 1 (23 Mar 2016) 11 httpsdoiorg101140epjdss13688-016-0072-6

[8] Despoina Chatzakou Nicolas Kourtellis Jeremy Blackburn EmilianoDe Cristofaro Gianluca Stringhini and Athena Vakali 2017 Hateis not binary Studying abusive behavior of gamergate on twitterProceedings of the 28th ACM conference on hypertext and social media65ndash74

[9] Despoina Chatzakou Nicolas Kourtellis Jeremy Blackburn EmilianoDe Cristofaro Gianluca Stringhini and Athena Vakali 2017 Meanbirds Detecting aggression and bullying on twitter Proceedings of the2017 ACM on web science conference 13ndash22

[10] Thomas Davidson Dana Warmsley Michael Macy and Ingmar Weber2017 Automated hate speech detection and the problem of offensivelanguage Proceedings of ICWSM (2017)

[11] Karthik Dinakar Birago Jones Catherine Havasi Henry Liebermanand Rosalind Picard 2012 Common sense reasoning for detectionprevention and mitigation of cyberbullying ACM Transactions onInteractive Intelligent Systems (TiiS) 2 3 (2012) 18

[12] Jacob Eisenstein Amr Ahmed and Eric P Xing 2011 Sparse additivegenerative models of text Proceedings of the 28th International Confer-ence on International Conference on Machine Learning 1041ndash1048

[13] Mai ElSherief Vivek Kulkarni Dana Nguyen William Yang Wangand Elizabeth Belding 2018 Hate Lingo A Target-based LinguisticAnalysis of Hate Speech in Social Media ICWSM (2018)

[14] Mai ElSherief Shirin Nilizadeh Dana Nguyen Giovanni Vigna andElizabeth Belding 2018 Peer to Peer Hate Hate Speech Instigatorsand Their Targets ICWSM (2018)

[15] Facebook 2016 Controversial Harmful and Hateful Speech on Face-book httpsgooglTWAHdr

[16] Antigoni-Maria Founta Constantinos Djouvas Despoina ChatzakouIlias Leontiadis Jeremy Blackburn Gianluca Stringhini Athena VakaliMichael Sirivianos and Nicolas Kourtellis 2018 Large Scale Crowd-sourcing and Characterization of Twitter Abusive Behavior Interna-tional Conference on Social Web and Media Log (2018)

[17] Howard Giles 2005 Intergroup communication Multiple perspectivesVol 2 Peter Lang

[18] Njagi Dennis Gitari Zhang Zuping Hanyurwimfura Damien andJun Long 2015 A lexicon-based approach for hate speech detectionInternational Journal of Multimedia and Ubiquitous Engineering 10 4(2015) 215ndash230

[19] Srijan Kumar William L Hamilton Jure Leskovec and Dan Jurafsky2018 Community interaction and conflict on the web Proceedings ofthe 2018 World Wide Web Conference on World Wide Web 933ndash943

[20] Irene Kwok and YuzhouWang 2013 Locate the Hate Detecting Tweetsagainst Blacks AAAI

[21] Altaf Mahmud Kazi Zubair Ahmed and Mumit Khan 2008 Detectingflames and insults in text (2008)

[22] Yashar Mehdad and Joel Tetreault 2016 Do Characters Abuse MoreThan Words Proceedings of the 17th Annual Meeting of the SpecialInterest Group on Discourse and Dialogue 299ndash303

[23] Fred Morstatter Juumlrgen Pfeffer Huan Liu and Kathleen M Carley2013 Is the Sample Good Enough Comparing Data from TwitterrsquosStreaming API with Twitterrsquos Firehose ICWSM

[24] Chikashi Nobata Joel Tetreault Achint Thomas Yashar Mehdad andYi Chang 2016 Abusive language detection in online user contentProceedings of the 25th international conference on world wide web145ndash153

[25] Alexandra Olteanu Carlos Castillo Jeremy Boy and Kush R Varshney2018 The Effect of Extremist Violence on Hateful Speech Online arXivpreprint arXiv180405704 (2018)

[26] James W Pennebaker Ryan L Boyd Kayla Jordan and Kate Black-burn 2015 The development and psychometric properties of LIWC2015Technical Report

[27] Koustav Rudra Siddhartha Banerjee Niloy Ganguly Pawan GoyalMuhammad Imran and Prasenjit Mitra 2016 Summarizing situationaltweets in crisis scenario Proceedings of the 27th ACM Conference onHypertext and Social Media 137ndash147

[28] SafeHomeorg 2017 Hate on Social Media httpswwwsafehomeorgresourceshate-on-social-media

[29] Muzafer Sherif 2015 Group conflict and co-operation Their socialpsychology Psychology Press

[30] Leandro Arauacutejo Silva Mainack Mondal Denzil Correa Fabriacutecio Ben-evenuto and Ingmar Weber 2016 Analyzing the Targets of Hate inOnline Social Media ICWSM 687ndash690

[31] Henri Tajfel and John C Turner 1979 An integrative theory of in-tergroup conflict The social psychology of intergroup relations 33 47(1979) 74

[32] Duyu Tang Bing Qin Xiaocheng Feng and Ting Liu 2016 EffectiveLSTMs for target-dependent sentiment classification InternationalConference on Computational Linguistics (2016) 3298ndash3307

[33] Duyu Tang Bing Qin Xiaocheng Feng and Ting Liu 2016 Target-Dependent Sentiment Classification with Long Short Term Mem-ory Proceedings of COLING 2016 the 26th International Conferenceon Computational Linguistics Technical Papers abs151201100 (2016)3298acircĂŞ3307

[34] Ginka Toegel and Jean-Louis Barsoux 2012 How to become a betterleader MIT Sloan Management Review 53 3 (2012) 51ndash60

[35] Zeynep Tufekci 2014 Big Questions for Social Media Big Data Repre-sentativeness Validity and Other Methodological Pitfalls ICWSM 14(2014) 505ndash514

[36] Twitter 2016 Hateful Conduct Policy httpssupporttwittercomarticles20175050

[37] Cynthia Van Hee Els Lefever Ben Verhoeven Julie Mennes BartDesmet Guy De Pauw Walter Daelemans and Veacuteronique Hoste 2015Detection and fine-grained classification of cyberbullying events In-ternational Conference Recent Advances in Natural Language Processing(RANLP) 672ndash680

[38] Zeerak Waseem and Dirk Hovy 2016 Hateful Symbols or HatefulPeople Predictive Features for Hate Speech Detection on TwitterProceedings of the NAACL Student Research Workshop 88ndash93 httpwwwaclweborganthologyN16-2013

[39] Michael Wiegand Josef Ruppenhofer Anna Schmidt and ClaytonGreenberg 2018 Inducing a Lexicon of Abusive Wordsndasha Feature-Based Approach Proceedings of the 2018 Conference of the North Amer-ican Chapter of the Association for Computational Linguistics HumanLanguage Technologies Volume 1 (Long Papers) 1 1046ndash1056

IUI Workshopsrsquo19 March 20 2019 Los Angeles USA Khosla et al

Madrid FC community seem to avoid hate speech against FCBarcelona (16)

What are their key personality traits To study the key char-acteristics of the personalities of HUs in our domain we usethe Twitter REST API to fetch the most recent 3200 tweetsfor each account We exclude retweets as they might not re-flect authorrsquos point of view and use IBM Watson PersonalityInsights API1 for this analysis It outputs a normalized per-centile score for the characteristic We study the results of theBig Five personality model the most widely used model forgenerally describing how a person engages with the worldThe model includes five primary dimensions AgreeablenessConscientiousness Extraversion Neuroticism and Open-ness [2]Figure 5 shows the distribution of scores of the Big Five

personality traits for Tmadrid We find that HS(Sall) andHS(Sbarca) have more similar personalities to each otherthan general mentions Both HS(Sall) and HS(Sbarca) ex-hibit lower Agreeableness than general mentions Prior work[34] associates lower Agreeableness scores with suspiciousand antagonistic behaviors Our results indicate that HS(Sall)and HS(Sbarca) are more self-focused contrary cautious ofothers and lack empathy For Conscientiousness HS(Sall)and HS(Sbarca) generally have lower scores than generalmentions Our results suggest that these users are laid backless goal-oriented and tend to disregard rules Low Extraver-sion scores for both HS(Sall) and HS(Sbarca) show that theyare less sociable less assertive and more within themselvesHS(Sall) and HS(Sbarca) have slightly higher but statisti-cally significant scores for Neuroticism which indicates thatthey are more susceptible to stress and are more likely to ex-perience anxiety jealousy and anger However for Opennessthe distributions for HS(Sall) and HS(Sbarca) are close togeneral mentions (with median of approximately 019) Weobserve a similar trend for Tbarca but omit here for brevity

Characterizing hateful tweetsAre these hate tweets popular We next investigate the popu-larity of target-specific hate tweets in our domain We usethe retweet-count of tweets to judge their popularity We ob-serve that for target community FC Barcelona (Tbarca) hate-ful tweets from Real Madrid ie HS(Smadrid Tbarca) areretweeted less than general hateful tweets HS(Sall Tbarca)(with mean = 019 and 053 respectively) which in turn aresubstantially less popular than non-hateful tweets (mean =547)

Content Characteristics We use SAGE [12] to analyze salientwords that characterize different types of tweets SAGE at-tempts to find salient terms in a text (child) with respect to

1httpswwwibmcomwatsonservicespersonality-insights

Exp Top 10 Salient Words

(i) fk bch fked fking adore cnt yer kidcheating idiot

(ii) fk fked fking bch cnt bullst adoreshty sht cheating

(iii)granada rampant messi bartomeudimiteya

valverde forcabarca viscabarca yermadridiots penaldo

Table 3 Top 10 salient words learned by SAGE for differentexperiments with Tmadrid Note the presence of barca

specific keywords (bold) in (iii)

some base content (base) It creates clean topic models bytaking into account the additive effects and combines multi-ple generative facets like topic and perspective distributionof words In this analysis we conduct three experiments (i)child = HS(Sall) base = tweets which mention the targetcommunity (ii) child = HS(Srival)base = tweets which men-tion the target community and (iii) child = HS(Srival) base= HS(Sall) We look at the top 10 salient words learned forthe above-mentioned experiments (Table 3)

As a whole both (i) and (ii) contain similar salient wordsThese words are mostly cuss words which is to be expectedThe top salient words in (iii) contain mentions of the entitiesof the source community On closer look we find that thesetweets try to demean the target community (eg playersmanagers or ideology etc) in an attempt to glorify the sourcecommunity For example this hate tweet by a Real Madrid FCsupporter against FC Barcelona king dem ronaldo king demleft and right salute d kingi want to take this opportunity andsay fk all barcelona fans fcbarcelona _es tries to glorifyRonaldo (an ex Real Madrid FC player) by calling him a KingSuch a pattern of comparison is not visible in HS(Sall) whichmostly focuses on the negatives of the target communityFor example this hate tweet against FC Barcelona from auser who is not a Real Madrid FC community member DearFCBarcelona please take your sht (Bellerin) back Please

Psycholinguistic Analysis We use LIWC2015 [26] for a fullpsycholinguistic analysis We look at the following dimen-sions summary scores personal pronouns and negativeemotions2 In Figure 6 we can see that tweets with general(non-hate) mentions (NHM) of Real Madrid FC differ signifi-cantly from hateful tweets Summary scores suggest that gen-eral tweets display higher values of tone than HS(Tmadrid)suggesting that targeted hate speech is more hostile HS(Tmadrid) contains higher number of pronouns and is angrierthan general tweets Also HS(Tmadrid) is more informal andexpectedly contain more swear words It contains shortersentences and uses more dictionary words on average as

2LIWC2015 language manual [26] provides a detailed description for thesedimensions

Understanding Community Rivalry on Social Media IUI Workshopsrsquo19 March 20 2019 Los Angeles USA

Figure 5 Distribution of scores for the Big Five personality traits for users who posted tweets that mention Real Madrid FCGeneral Mentions are tweets that mention the target community or its associated entities

Tone Analytic Clout WPS Dic0

20

40

60

80

100

Score

HS(Sall) HS(Sbarca) NHM

(a) Summary

i we they you shehe0

05

1

15

2

Score

HS(Sall) HS(Sbarca) NHM

(b) Personal Pronouns

anger sad anx0

2

4

6

Score

HS(Sall) HS(Sbarca) NHM

(c) Negative Emotions

Figure 6 Psycholinguistic Analysis of tweets whichmention Real Madrid FC (Tmadrid)

against the non-hate tweets mentioning Real Madrid FC Asimilar trend is observed for Tbarca (omitted for brevity)

7 DISCUSSION AND CONCLUSIONSIn this work we provide a novel view of hate exchangebetween different communities in the football domain Wedesign a two-step model to detect target-specific hate speechUsing causal inference methodologies we are able to mea-sure the effect of external events on hate speech on social

media We show how rival communities post disproportion-ately high amount of hate during events which have a directimpact on their teamrsquos interests We find that hateful usersin our domain also post hate speech in general They donot post hate in multiple events and do not enjoy generouspopularity on social media We show that their personalitycharacteristics are significantly different from general userswho post about the target community Our analysis showsthat hate tweets from rival community members differ intheir theme from general hate tweets towards the targetcommunity They try to glorify their teamrsquos players playingstyle or ideology while demeaning the target communityHowever the psycholinguistic analysis of the hateful tweetssuggests that content from rival community does not differfrom general tweets in terms of the emotional content tonepronoun usage or swear wordsNonetheless our analysis has limitations Recent studies

by Tufekci [35] and Morstatter et al [23] have discussed thesample quality of the Twitter API Since our analysis relies onkeyword-based methods for retrieval of explicit hate speechwe cannot claim to have captured a complete representationof the hate exchange on Twitter However our main objectivewas to characterize hateful users and tweets in the sportsdomain with high precision and we believe that our carefulfiltering and classification models were able to do so

REFERENCES[1] Pinkesh Badjatiya Shashank Gupta Manish Gupta and Vasudeva

Varma 2017 Deep learning for hate speech detection in tweets Pro-ceedings of the 26th International Conference on World Wide Web Com-panion 759ndash760

IUI Workshopsrsquo19 March 20 2019 Los Angeles USA Khosla et al

[2] Murray R Barrick and Michael K Mount 1991 The big five personalitydimensions and job performance ameta-analysis Personnel psychology44 1 (1991) 1ndash26

[3] Vaacuteclav Belaacutek Samantha Lam and Conor Hayes 2012 Cross-Community Influence in Discussion Fora ICWSM 12 (2012) 34ndash41

[4] Susan Benesch 2014 Countering dangerous speech to prevent massviolence during KenyaacircĂŹs 2013 elections Final Report (2014) 1ndash26

[5] Kay H Brodersen Fabian Gallusser Jim Koehler Nicolas Remy andSteven L Scott 2015 Inferring causal impact using Bayesian structuraltime-series models Annals of Applied Statistics 9 (2015) 247ndash274

[6] Pete Burnap Omer F Rana Nick Avis Matthew Williams WilliamHousley Adam Edwards Jeffrey Morgan and Luke Sloan 2015 Detect-ing tension in online communities with computational Twitter analysisTechnological Forecasting and Social Change 95 (2015) 96ndash108

[7] Pete Burnap and Matthew L Williams 2016 Us and them identifyingcyber hate on Twitter across multiple protected characteristics EPJData Science 5 1 (23 Mar 2016) 11 httpsdoiorg101140epjdss13688-016-0072-6

[8] Despoina Chatzakou Nicolas Kourtellis Jeremy Blackburn EmilianoDe Cristofaro Gianluca Stringhini and Athena Vakali 2017 Hateis not binary Studying abusive behavior of gamergate on twitterProceedings of the 28th ACM conference on hypertext and social media65ndash74

[9] Despoina Chatzakou Nicolas Kourtellis Jeremy Blackburn EmilianoDe Cristofaro Gianluca Stringhini and Athena Vakali 2017 Meanbirds Detecting aggression and bullying on twitter Proceedings of the2017 ACM on web science conference 13ndash22

[10] Thomas Davidson Dana Warmsley Michael Macy and Ingmar Weber2017 Automated hate speech detection and the problem of offensivelanguage Proceedings of ICWSM (2017)

[11] Karthik Dinakar Birago Jones Catherine Havasi Henry Liebermanand Rosalind Picard 2012 Common sense reasoning for detectionprevention and mitigation of cyberbullying ACM Transactions onInteractive Intelligent Systems (TiiS) 2 3 (2012) 18

[12] Jacob Eisenstein Amr Ahmed and Eric P Xing 2011 Sparse additivegenerative models of text Proceedings of the 28th International Confer-ence on International Conference on Machine Learning 1041ndash1048

[13] Mai ElSherief Vivek Kulkarni Dana Nguyen William Yang Wangand Elizabeth Belding 2018 Hate Lingo A Target-based LinguisticAnalysis of Hate Speech in Social Media ICWSM (2018)

[14] Mai ElSherief Shirin Nilizadeh Dana Nguyen Giovanni Vigna andElizabeth Belding 2018 Peer to Peer Hate Hate Speech Instigatorsand Their Targets ICWSM (2018)

[15] Facebook 2016 Controversial Harmful and Hateful Speech on Face-book httpsgooglTWAHdr

[16] Antigoni-Maria Founta Constantinos Djouvas Despoina ChatzakouIlias Leontiadis Jeremy Blackburn Gianluca Stringhini Athena VakaliMichael Sirivianos and Nicolas Kourtellis 2018 Large Scale Crowd-sourcing and Characterization of Twitter Abusive Behavior Interna-tional Conference on Social Web and Media Log (2018)

[17] Howard Giles 2005 Intergroup communication Multiple perspectivesVol 2 Peter Lang

[18] Njagi Dennis Gitari Zhang Zuping Hanyurwimfura Damien andJun Long 2015 A lexicon-based approach for hate speech detectionInternational Journal of Multimedia and Ubiquitous Engineering 10 4(2015) 215ndash230

[19] Srijan Kumar William L Hamilton Jure Leskovec and Dan Jurafsky2018 Community interaction and conflict on the web Proceedings ofthe 2018 World Wide Web Conference on World Wide Web 933ndash943

[20] Irene Kwok and YuzhouWang 2013 Locate the Hate Detecting Tweetsagainst Blacks AAAI

[21] Altaf Mahmud Kazi Zubair Ahmed and Mumit Khan 2008 Detectingflames and insults in text (2008)

[22] Yashar Mehdad and Joel Tetreault 2016 Do Characters Abuse MoreThan Words Proceedings of the 17th Annual Meeting of the SpecialInterest Group on Discourse and Dialogue 299ndash303

[23] Fred Morstatter Juumlrgen Pfeffer Huan Liu and Kathleen M Carley2013 Is the Sample Good Enough Comparing Data from TwitterrsquosStreaming API with Twitterrsquos Firehose ICWSM

[24] Chikashi Nobata Joel Tetreault Achint Thomas Yashar Mehdad andYi Chang 2016 Abusive language detection in online user contentProceedings of the 25th international conference on world wide web145ndash153

[25] Alexandra Olteanu Carlos Castillo Jeremy Boy and Kush R Varshney2018 The Effect of Extremist Violence on Hateful Speech Online arXivpreprint arXiv180405704 (2018)

[26] James W Pennebaker Ryan L Boyd Kayla Jordan and Kate Black-burn 2015 The development and psychometric properties of LIWC2015Technical Report

[27] Koustav Rudra Siddhartha Banerjee Niloy Ganguly Pawan GoyalMuhammad Imran and Prasenjit Mitra 2016 Summarizing situationaltweets in crisis scenario Proceedings of the 27th ACM Conference onHypertext and Social Media 137ndash147

[28] SafeHomeorg 2017 Hate on Social Media httpswwwsafehomeorgresourceshate-on-social-media

[29] Muzafer Sherif 2015 Group conflict and co-operation Their socialpsychology Psychology Press

[30] Leandro Arauacutejo Silva Mainack Mondal Denzil Correa Fabriacutecio Ben-evenuto and Ingmar Weber 2016 Analyzing the Targets of Hate inOnline Social Media ICWSM 687ndash690

[31] Henri Tajfel and John C Turner 1979 An integrative theory of in-tergroup conflict The social psychology of intergroup relations 33 47(1979) 74

[32] Duyu Tang Bing Qin Xiaocheng Feng and Ting Liu 2016 EffectiveLSTMs for target-dependent sentiment classification InternationalConference on Computational Linguistics (2016) 3298ndash3307

[33] Duyu Tang Bing Qin Xiaocheng Feng and Ting Liu 2016 Target-Dependent Sentiment Classification with Long Short Term Mem-ory Proceedings of COLING 2016 the 26th International Conferenceon Computational Linguistics Technical Papers abs151201100 (2016)3298acircĂŞ3307

[34] Ginka Toegel and Jean-Louis Barsoux 2012 How to become a betterleader MIT Sloan Management Review 53 3 (2012) 51ndash60

[35] Zeynep Tufekci 2014 Big Questions for Social Media Big Data Repre-sentativeness Validity and Other Methodological Pitfalls ICWSM 14(2014) 505ndash514

[36] Twitter 2016 Hateful Conduct Policy httpssupporttwittercomarticles20175050

[37] Cynthia Van Hee Els Lefever Ben Verhoeven Julie Mennes BartDesmet Guy De Pauw Walter Daelemans and Veacuteronique Hoste 2015Detection and fine-grained classification of cyberbullying events In-ternational Conference Recent Advances in Natural Language Processing(RANLP) 672ndash680

[38] Zeerak Waseem and Dirk Hovy 2016 Hateful Symbols or HatefulPeople Predictive Features for Hate Speech Detection on TwitterProceedings of the NAACL Student Research Workshop 88ndash93 httpwwwaclweborganthologyN16-2013

[39] Michael Wiegand Josef Ruppenhofer Anna Schmidt and ClaytonGreenberg 2018 Inducing a Lexicon of Abusive Wordsndasha Feature-Based Approach Proceedings of the 2018 Conference of the North Amer-ican Chapter of the Association for Computational Linguistics HumanLanguage Technologies Volume 1 (Long Papers) 1 1046ndash1056

Understanding Community Rivalry on Social Media IUI Workshopsrsquo19 March 20 2019 Los Angeles USA

Figure 5 Distribution of scores for the Big Five personality traits for users who posted tweets that mention Real Madrid FCGeneral Mentions are tweets that mention the target community or its associated entities

Tone Analytic Clout WPS Dic0

20

40

60

80

100

Score

HS(Sall) HS(Sbarca) NHM

(a) Summary

i we they you shehe0

05

1

15

2

Score

HS(Sall) HS(Sbarca) NHM

(b) Personal Pronouns

anger sad anx0

2

4

6

Score

HS(Sall) HS(Sbarca) NHM

(c) Negative Emotions

Figure 6 Psycholinguistic Analysis of tweets whichmention Real Madrid FC (Tmadrid)

against the non-hate tweets mentioning Real Madrid FC Asimilar trend is observed for Tbarca (omitted for brevity)

7 DISCUSSION AND CONCLUSIONSIn this work we provide a novel view of hate exchangebetween different communities in the football domain Wedesign a two-step model to detect target-specific hate speechUsing causal inference methodologies we are able to mea-sure the effect of external events on hate speech on social

media We show how rival communities post disproportion-ately high amount of hate during events which have a directimpact on their teamrsquos interests We find that hateful usersin our domain also post hate speech in general They donot post hate in multiple events and do not enjoy generouspopularity on social media We show that their personalitycharacteristics are significantly different from general userswho post about the target community Our analysis showsthat hate tweets from rival community members differ intheir theme from general hate tweets towards the targetcommunity They try to glorify their teamrsquos players playingstyle or ideology while demeaning the target communityHowever the psycholinguistic analysis of the hateful tweetssuggests that content from rival community does not differfrom general tweets in terms of the emotional content tonepronoun usage or swear wordsNonetheless our analysis has limitations Recent studies

by Tufekci [35] and Morstatter et al [23] have discussed thesample quality of the Twitter API Since our analysis relies onkeyword-based methods for retrieval of explicit hate speechwe cannot claim to have captured a complete representationof the hate exchange on Twitter However our main objectivewas to characterize hateful users and tweets in the sportsdomain with high precision and we believe that our carefulfiltering and classification models were able to do so

REFERENCES[1] Pinkesh Badjatiya Shashank Gupta Manish Gupta and Vasudeva

Varma 2017 Deep learning for hate speech detection in tweets Pro-ceedings of the 26th International Conference on World Wide Web Com-panion 759ndash760

IUI Workshopsrsquo19 March 20 2019 Los Angeles USA Khosla et al

[2] Murray R Barrick and Michael K Mount 1991 The big five personalitydimensions and job performance ameta-analysis Personnel psychology44 1 (1991) 1ndash26

[3] Vaacuteclav Belaacutek Samantha Lam and Conor Hayes 2012 Cross-Community Influence in Discussion Fora ICWSM 12 (2012) 34ndash41

[4] Susan Benesch 2014 Countering dangerous speech to prevent massviolence during KenyaacircĂŹs 2013 elections Final Report (2014) 1ndash26

[5] Kay H Brodersen Fabian Gallusser Jim Koehler Nicolas Remy andSteven L Scott 2015 Inferring causal impact using Bayesian structuraltime-series models Annals of Applied Statistics 9 (2015) 247ndash274

[6] Pete Burnap Omer F Rana Nick Avis Matthew Williams WilliamHousley Adam Edwards Jeffrey Morgan and Luke Sloan 2015 Detect-ing tension in online communities with computational Twitter analysisTechnological Forecasting and Social Change 95 (2015) 96ndash108

[7] Pete Burnap and Matthew L Williams 2016 Us and them identifyingcyber hate on Twitter across multiple protected characteristics EPJData Science 5 1 (23 Mar 2016) 11 httpsdoiorg101140epjdss13688-016-0072-6

[8] Despoina Chatzakou Nicolas Kourtellis Jeremy Blackburn EmilianoDe Cristofaro Gianluca Stringhini and Athena Vakali 2017 Hateis not binary Studying abusive behavior of gamergate on twitterProceedings of the 28th ACM conference on hypertext and social media65ndash74

[9] Despoina Chatzakou Nicolas Kourtellis Jeremy Blackburn EmilianoDe Cristofaro Gianluca Stringhini and Athena Vakali 2017 Meanbirds Detecting aggression and bullying on twitter Proceedings of the2017 ACM on web science conference 13ndash22

[10] Thomas Davidson Dana Warmsley Michael Macy and Ingmar Weber2017 Automated hate speech detection and the problem of offensivelanguage Proceedings of ICWSM (2017)

[11] Karthik Dinakar Birago Jones Catherine Havasi Henry Liebermanand Rosalind Picard 2012 Common sense reasoning for detectionprevention and mitigation of cyberbullying ACM Transactions onInteractive Intelligent Systems (TiiS) 2 3 (2012) 18

[12] Jacob Eisenstein Amr Ahmed and Eric P Xing 2011 Sparse additivegenerative models of text Proceedings of the 28th International Confer-ence on International Conference on Machine Learning 1041ndash1048

[13] Mai ElSherief Vivek Kulkarni Dana Nguyen William Yang Wangand Elizabeth Belding 2018 Hate Lingo A Target-based LinguisticAnalysis of Hate Speech in Social Media ICWSM (2018)

[14] Mai ElSherief Shirin Nilizadeh Dana Nguyen Giovanni Vigna andElizabeth Belding 2018 Peer to Peer Hate Hate Speech Instigatorsand Their Targets ICWSM (2018)

[15] Facebook 2016 Controversial Harmful and Hateful Speech on Face-book httpsgooglTWAHdr

[16] Antigoni-Maria Founta Constantinos Djouvas Despoina ChatzakouIlias Leontiadis Jeremy Blackburn Gianluca Stringhini Athena VakaliMichael Sirivianos and Nicolas Kourtellis 2018 Large Scale Crowd-sourcing and Characterization of Twitter Abusive Behavior Interna-tional Conference on Social Web and Media Log (2018)

[17] Howard Giles 2005 Intergroup communication Multiple perspectivesVol 2 Peter Lang

[18] Njagi Dennis Gitari Zhang Zuping Hanyurwimfura Damien andJun Long 2015 A lexicon-based approach for hate speech detectionInternational Journal of Multimedia and Ubiquitous Engineering 10 4(2015) 215ndash230

[19] Srijan Kumar William L Hamilton Jure Leskovec and Dan Jurafsky2018 Community interaction and conflict on the web Proceedings ofthe 2018 World Wide Web Conference on World Wide Web 933ndash943

[20] Irene Kwok and YuzhouWang 2013 Locate the Hate Detecting Tweetsagainst Blacks AAAI

[21] Altaf Mahmud Kazi Zubair Ahmed and Mumit Khan 2008 Detectingflames and insults in text (2008)

[22] Yashar Mehdad and Joel Tetreault 2016 Do Characters Abuse MoreThan Words Proceedings of the 17th Annual Meeting of the SpecialInterest Group on Discourse and Dialogue 299ndash303

[23] Fred Morstatter Juumlrgen Pfeffer Huan Liu and Kathleen M Carley2013 Is the Sample Good Enough Comparing Data from TwitterrsquosStreaming API with Twitterrsquos Firehose ICWSM

[24] Chikashi Nobata Joel Tetreault Achint Thomas Yashar Mehdad andYi Chang 2016 Abusive language detection in online user contentProceedings of the 25th international conference on world wide web145ndash153

[25] Alexandra Olteanu Carlos Castillo Jeremy Boy and Kush R Varshney2018 The Effect of Extremist Violence on Hateful Speech Online arXivpreprint arXiv180405704 (2018)

[26] James W Pennebaker Ryan L Boyd Kayla Jordan and Kate Black-burn 2015 The development and psychometric properties of LIWC2015Technical Report

[27] Koustav Rudra Siddhartha Banerjee Niloy Ganguly Pawan GoyalMuhammad Imran and Prasenjit Mitra 2016 Summarizing situationaltweets in crisis scenario Proceedings of the 27th ACM Conference onHypertext and Social Media 137ndash147

[28] SafeHomeorg 2017 Hate on Social Media httpswwwsafehomeorgresourceshate-on-social-media

[29] Muzafer Sherif 2015 Group conflict and co-operation Their socialpsychology Psychology Press

[30] Leandro Arauacutejo Silva Mainack Mondal Denzil Correa Fabriacutecio Ben-evenuto and Ingmar Weber 2016 Analyzing the Targets of Hate inOnline Social Media ICWSM 687ndash690

[31] Henri Tajfel and John C Turner 1979 An integrative theory of in-tergroup conflict The social psychology of intergroup relations 33 47(1979) 74

[32] Duyu Tang Bing Qin Xiaocheng Feng and Ting Liu 2016 EffectiveLSTMs for target-dependent sentiment classification InternationalConference on Computational Linguistics (2016) 3298ndash3307

[33] Duyu Tang Bing Qin Xiaocheng Feng and Ting Liu 2016 Target-Dependent Sentiment Classification with Long Short Term Mem-ory Proceedings of COLING 2016 the 26th International Conferenceon Computational Linguistics Technical Papers abs151201100 (2016)3298acircĂŞ3307

[34] Ginka Toegel and Jean-Louis Barsoux 2012 How to become a betterleader MIT Sloan Management Review 53 3 (2012) 51ndash60

[35] Zeynep Tufekci 2014 Big Questions for Social Media Big Data Repre-sentativeness Validity and Other Methodological Pitfalls ICWSM 14(2014) 505ndash514

[36] Twitter 2016 Hateful Conduct Policy httpssupporttwittercomarticles20175050

[37] Cynthia Van Hee Els Lefever Ben Verhoeven Julie Mennes BartDesmet Guy De Pauw Walter Daelemans and Veacuteronique Hoste 2015Detection and fine-grained classification of cyberbullying events In-ternational Conference Recent Advances in Natural Language Processing(RANLP) 672ndash680

[38] Zeerak Waseem and Dirk Hovy 2016 Hateful Symbols or HatefulPeople Predictive Features for Hate Speech Detection on TwitterProceedings of the NAACL Student Research Workshop 88ndash93 httpwwwaclweborganthologyN16-2013

[39] Michael Wiegand Josef Ruppenhofer Anna Schmidt and ClaytonGreenberg 2018 Inducing a Lexicon of Abusive Wordsndasha Feature-Based Approach Proceedings of the 2018 Conference of the North Amer-ican Chapter of the Association for Computational Linguistics HumanLanguage Technologies Volume 1 (Long Papers) 1 1046ndash1056

IUI Workshopsrsquo19 March 20 2019 Los Angeles USA Khosla et al

[2] Murray R Barrick and Michael K Mount 1991 The big five personalitydimensions and job performance ameta-analysis Personnel psychology44 1 (1991) 1ndash26

[3] Vaacuteclav Belaacutek Samantha Lam and Conor Hayes 2012 Cross-Community Influence in Discussion Fora ICWSM 12 (2012) 34ndash41

[4] Susan Benesch 2014 Countering dangerous speech to prevent massviolence during KenyaacircĂŹs 2013 elections Final Report (2014) 1ndash26

[5] Kay H Brodersen Fabian Gallusser Jim Koehler Nicolas Remy andSteven L Scott 2015 Inferring causal impact using Bayesian structuraltime-series models Annals of Applied Statistics 9 (2015) 247ndash274

[6] Pete Burnap Omer F Rana Nick Avis Matthew Williams WilliamHousley Adam Edwards Jeffrey Morgan and Luke Sloan 2015 Detect-ing tension in online communities with computational Twitter analysisTechnological Forecasting and Social Change 95 (2015) 96ndash108

[7] Pete Burnap and Matthew L Williams 2016 Us and them identifyingcyber hate on Twitter across multiple protected characteristics EPJData Science 5 1 (23 Mar 2016) 11 httpsdoiorg101140epjdss13688-016-0072-6

[8] Despoina Chatzakou Nicolas Kourtellis Jeremy Blackburn EmilianoDe Cristofaro Gianluca Stringhini and Athena Vakali 2017 Hateis not binary Studying abusive behavior of gamergate on twitterProceedings of the 28th ACM conference on hypertext and social media65ndash74

[9] Despoina Chatzakou Nicolas Kourtellis Jeremy Blackburn EmilianoDe Cristofaro Gianluca Stringhini and Athena Vakali 2017 Meanbirds Detecting aggression and bullying on twitter Proceedings of the2017 ACM on web science conference 13ndash22

[10] Thomas Davidson Dana Warmsley Michael Macy and Ingmar Weber2017 Automated hate speech detection and the problem of offensivelanguage Proceedings of ICWSM (2017)

[11] Karthik Dinakar Birago Jones Catherine Havasi Henry Liebermanand Rosalind Picard 2012 Common sense reasoning for detectionprevention and mitigation of cyberbullying ACM Transactions onInteractive Intelligent Systems (TiiS) 2 3 (2012) 18

[12] Jacob Eisenstein Amr Ahmed and Eric P Xing 2011 Sparse additivegenerative models of text Proceedings of the 28th International Confer-ence on International Conference on Machine Learning 1041ndash1048

[13] Mai ElSherief Vivek Kulkarni Dana Nguyen William Yang Wangand Elizabeth Belding 2018 Hate Lingo A Target-based LinguisticAnalysis of Hate Speech in Social Media ICWSM (2018)

[14] Mai ElSherief Shirin Nilizadeh Dana Nguyen Giovanni Vigna andElizabeth Belding 2018 Peer to Peer Hate Hate Speech Instigatorsand Their Targets ICWSM (2018)

[15] Facebook 2016 Controversial Harmful and Hateful Speech on Face-book httpsgooglTWAHdr

[16] Antigoni-Maria Founta Constantinos Djouvas Despoina ChatzakouIlias Leontiadis Jeremy Blackburn Gianluca Stringhini Athena VakaliMichael Sirivianos and Nicolas Kourtellis 2018 Large Scale Crowd-sourcing and Characterization of Twitter Abusive Behavior Interna-tional Conference on Social Web and Media Log (2018)

[17] Howard Giles 2005 Intergroup communication Multiple perspectivesVol 2 Peter Lang

[18] Njagi Dennis Gitari Zhang Zuping Hanyurwimfura Damien andJun Long 2015 A lexicon-based approach for hate speech detectionInternational Journal of Multimedia and Ubiquitous Engineering 10 4(2015) 215ndash230

[19] Srijan Kumar William L Hamilton Jure Leskovec and Dan Jurafsky2018 Community interaction and conflict on the web Proceedings ofthe 2018 World Wide Web Conference on World Wide Web 933ndash943

[20] Irene Kwok and YuzhouWang 2013 Locate the Hate Detecting Tweetsagainst Blacks AAAI

[21] Altaf Mahmud Kazi Zubair Ahmed and Mumit Khan 2008 Detectingflames and insults in text (2008)

[22] Yashar Mehdad and Joel Tetreault 2016 Do Characters Abuse MoreThan Words Proceedings of the 17th Annual Meeting of the SpecialInterest Group on Discourse and Dialogue 299ndash303

[23] Fred Morstatter Juumlrgen Pfeffer Huan Liu and Kathleen M Carley2013 Is the Sample Good Enough Comparing Data from TwitterrsquosStreaming API with Twitterrsquos Firehose ICWSM

[24] Chikashi Nobata Joel Tetreault Achint Thomas Yashar Mehdad andYi Chang 2016 Abusive language detection in online user contentProceedings of the 25th international conference on world wide web145ndash153

[25] Alexandra Olteanu Carlos Castillo Jeremy Boy and Kush R Varshney2018 The Effect of Extremist Violence on Hateful Speech Online arXivpreprint arXiv180405704 (2018)

[26] James W Pennebaker Ryan L Boyd Kayla Jordan and Kate Black-burn 2015 The development and psychometric properties of LIWC2015Technical Report

[27] Koustav Rudra Siddhartha Banerjee Niloy Ganguly Pawan GoyalMuhammad Imran and Prasenjit Mitra 2016 Summarizing situationaltweets in crisis scenario Proceedings of the 27th ACM Conference onHypertext and Social Media 137ndash147

[28] SafeHomeorg 2017 Hate on Social Media httpswwwsafehomeorgresourceshate-on-social-media

[29] Muzafer Sherif 2015 Group conflict and co-operation Their socialpsychology Psychology Press

[30] Leandro Arauacutejo Silva Mainack Mondal Denzil Correa Fabriacutecio Ben-evenuto and Ingmar Weber 2016 Analyzing the Targets of Hate inOnline Social Media ICWSM 687ndash690

[31] Henri Tajfel and John C Turner 1979 An integrative theory of in-tergroup conflict The social psychology of intergroup relations 33 47(1979) 74

[32] Duyu Tang Bing Qin Xiaocheng Feng and Ting Liu 2016 EffectiveLSTMs for target-dependent sentiment classification InternationalConference on Computational Linguistics (2016) 3298ndash3307

[33] Duyu Tang Bing Qin Xiaocheng Feng and Ting Liu 2016 Target-Dependent Sentiment Classification with Long Short Term Mem-ory Proceedings of COLING 2016 the 26th International Conferenceon Computational Linguistics Technical Papers abs151201100 (2016)3298acircĂŞ3307

[34] Ginka Toegel and Jean-Louis Barsoux 2012 How to become a betterleader MIT Sloan Management Review 53 3 (2012) 51ndash60

[35] Zeynep Tufekci 2014 Big Questions for Social Media Big Data Repre-sentativeness Validity and Other Methodological Pitfalls ICWSM 14(2014) 505ndash514

[36] Twitter 2016 Hateful Conduct Policy httpssupporttwittercomarticles20175050

[37] Cynthia Van Hee Els Lefever Ben Verhoeven Julie Mennes BartDesmet Guy De Pauw Walter Daelemans and Veacuteronique Hoste 2015Detection and fine-grained classification of cyberbullying events In-ternational Conference Recent Advances in Natural Language Processing(RANLP) 672ndash680

[38] Zeerak Waseem and Dirk Hovy 2016 Hateful Symbols or HatefulPeople Predictive Features for Hate Speech Detection on TwitterProceedings of the NAACL Student Research Workshop 88ndash93 httpwwwaclweborganthologyN16-2013

[39] Michael Wiegand Josef Ruppenhofer Anna Schmidt and ClaytonGreenberg 2018 Inducing a Lexicon of Abusive Wordsndasha Feature-Based Approach Proceedings of the 2018 Conference of the North Amer-ican Chapter of the Association for Computational Linguistics HumanLanguage Technologies Volume 1 (Long Papers) 1 1046ndash1056