social media crawling and mining seminar (motivation part)

Lecture @ International Hellenic UniversityThessaloniki, 8 May 2014

Social Media Crawling and MiningMotivation – Use CasesSymeon (Akis) Papadopoulos, Manos Schinas, Katerina Iliakopoulou, Yiannis KompatsiarisInformation Technologies Institute (ITI)Centre for Research & Technologies Hellas (CERTH)

MSDM 2014, Athens Social Data and Multimedia Analytics #2

IntroductionMotivationExample ApplicationsConceptual ArchitectureChallenges

MSDM 2014, Athens Social Data and Multimedia Analytics

http://www.puzzlemarketer.com/digital-social-brands-in-60-seconds/ (Apr, 2012)


Social Networks as Real-Life Sensors• Social Networks is a data source with an

extremely dynamic nature that reflects events and the evolution of community focus (user’s interests)

• Huge smartphones and mobile devices penetration provides real-time and location-based user feedback

• Transform individually rare but collectively frequent media to meaningful topics, events, points of interest, emotional states and social connections

• Present in an efficient way for a variety of applications (news, marketing, entertainment)


Pope Francis

Pope Benedict

2007: iPhone release

2008: Android release

2010: iPad release

http://petapixel.com/2013/03/14/a-starry-sea-of-cameras-at-the-unveiling-of-pope-francis/


Social Networks as Graphs


Social Networks as Graphs

“Social networks have emergent properties. Emergent properties are new attributes of a whole that arise from the interaction and interconnection of the parts”

•Emotions, Health, Sexual relationships do not depend just on our connections (e.g. number of them) but on our position - structure in the social graph

– Central – Hub– Outlier– Transitivity (connections between

friends)


Examples - Science

Xin Jin, Andrew Gallagher, Liangliang Cao, Jiebo Luo, and Jiawei Han. The wisdom of social multimedia: using flickr for prediction and forecast, International conference on Multimedia (MM '10). ACM.

8

“…if you're more than 100 km away from the epicenter [of an earthquake] you can read about the quake on twitter before it hits you…”


Example – News (Boston bombing)

#9

“Following the Boston Marathon bombings, one quarter of Americans reportedly looked to Facebook, Twitter and other social networking sites for information, according to The Pew Research Center. When the Boston Police Department posted its final “CAPTURED!!!” tweet of the manhunt, more than 140,000 people retweeted it.”

“Authorities have recognized that one the first places people go in events like this is to social media, to see what the crowd is saying about what to do next”

"I have been following my friend's Facebook [account] who is near the scene and she is updating everyone before it even gets to the news”


Events - Festivals

#10http://www.eventmanagerblog.com/uploads/2012/12/event-technology-infographic.jpg


API Wrapper

Website Wrapper

Scheduler

CRAWLING

Visual Indexing

Near-duplicates

Text Indexing

INDEXING

Media Fetcher

SNA

Sentiment - Influence

Trends - Topics

MINING

Model Building

Concepts

Relevance

Diversity

Popularity

RANKING

Veracity

Crawling Specs

Sources

Interaction

Responsiveness

Aggregation

VISUALIZATION

Aesthetics

Conceptual Architecture


Challenges – Content (Mining)

• Multi-modality: e.g. image + tags

• Rich social context: spatio-temporal, social connections, relations and social graph

• Inconsistent quality: noise, spam, ambiguity, fake, propaganda

• Huge volume: Massively produced and disseminated

• Multi-source: may be generated by different applications and user communities

• Also connected to other sources (e.g. LOD, web)

• Dynamic: Fast updates, real-time


Policy – Licensing – Legal challenges

• Fragmented access to data– Separate wrappers/APIs for each source (Twitter, Facebook, etc.)– Different data collection/crawling policies

• Limitations imposed by API providers (“Walled Gardens”)• Full access to data impossible or extremely expensive (e.g. see data

licensing plans for GNIP and DataSift• Non-transparent data access practices (e.g. access is provided to an

organization/person if they have a contact in Twitter) • Constant change of model and ToS of social APIs

– No backwards compatibility, additional development costs• Ephemeral nature of content

• Social search results often lead to removed content inconsistent and unreliable referencing

• User Privacy & Purpose of use• Fuzzy regulatory framework regarding mining user-contributed data


Social Sensor ProjectUse Cases


SocialSensor Project Objective

SocialSensor quickly surfaces trusted and relevant material from social media – with context.

DySCODySCO

behaviour

location

timecontent

usage

social context

Massive social mediaand unstructured web

Social media miningAggregation & indexing

News - InfotainmentPersonalised access

Ad-hoc P2P networks


The SocialSensor Vision

SocialSensor quickly surfaces trusted and relevant material from social media – with context.

•“quickly”: in real time•“surfaces”: automatically discovers, clusters and searches •“trusted”: automatic support in verification process•“relevant”: to the users, personalized•“material”: any material (text, image, audio, video = multimedia), aggregated with other sources (e.g. web)•“social media”: across all relevant social media platforms•“with context”: location, time, sentiment, influence


Conceptual Architecture and Main components

SEMANTIC MIDDLEWARE

Public Data

In-project Data

SEARCH & RECOMMENDATION

USER MODELLING & PRESENTATION

INDEXINGMINING

STORAGE

DATA COLLECTION / CRAWLING

• Real time dynamic topic and event clustering

• Trend, popularity and sentiment analysis

• Calculate trust/influence scores around people

• Personalized search, access & presentation based on social network interactions

• Semantic enrichment and discovery of services


Use Cases

Casual News application

Casual News Readers

Professional News application

Journalists, Editors, etc.

NEWS

EventLiveDashboard

Festival organizers

INFOTAINMENT

Social Media Walls

Festival attendants


“It has changed the way we do news”(MSN)

“Social media is the key place for emerging stories – internationally, nationally, locally” (BBC)

“Social media is transforming the way we do journalism”(New York Times)

Source: picture alliance / dpa


Source: Getty Images

“It’s really hard to find the nuggets of useful stuff in an ocean of content” (BBC)

“Things that aren’t relevant crowd out the content you are looking for” (MSN)

“The filters aren’t configurable enough” (CNN)


Verification was simpler in the past...

Source: Frank Grätz

#21


Infotainment• Events with large numbers

of visitors• Thessaloniki International

Film Festival – 80,000 viewers / 100,000

visitors in 10 days– 150 films, 350 screenings

• Discovery and presentation of relevant aggregated social media– Trending Topics– Sentiment– Tweet – film matching– Visualization (Social Walls)


Other Application Areas

• Science– Sociology, machine learning (machine as a teacher), computer vision

(annotation)• Tourism – Leisure – Culture

– Off-the-beaten path POI extraction• Marketing

– Brand monitoring, personalised ads• Prediction

– Politics: election results• News

– Topics, trends event detection• Others

– Environment, emergency response, energy saving, etc


Conclusions – Further topics• Social media data useful in many applications• Not all data always available (e.g. User queries, fb)

– Infrastructure– Policy - Privacy issues

• Real-time and scalable approaches– Efficiency of semantics and analysis vs. performance vs. infrastructure

• Fusion of various modalities– Content, social, temporal, location

• Verification & Linking other sources (web, Linked Open Data)• Visualization - Interfaces• Applications and commercialization• User engagement


Reusable results

• Starting point: http://www.socialsensor.eu/results – Deliverables– Publications – Datasets– Software– e-letter: http://stcsn.ieee.net/e-letter/vol-1-no-3

• Open-source projects (Apache License v2): https://github.com/socialsensor

– Data collection (stream-manager, storm-focused-crawler)– Indexing (framework-client, multimedia-indexing)– Mining (topic-detection, multimedia-analysis, community-evolution-

analysis, social-event-detection)


European Centre for Social Media

• Topics– Social media analytics– Verification– Visualisation– Applications in different domains

• Activities– Listings of project, results, institutions, events– Community building– Support/organise events– Common social media presence (e.g. LinkedIn)– Funding from subscriptions, training, commercialisation

– Supporting projects: SocialSensor, Reveal, MULTISENSOR, PHEME, DecarboNet, MWCC, uComp,

– Website: http://www.socialmediacentre.eu/ – Research-academic: STCSN http://stcsn.ieee.net/

Thank you for your [email protected]

http://mklab.iti.gr

social media crawling and mining seminar (motivation part)

Technology

ihu socialsensor seminar

social web

social media crawling

graphs social networks

social connections present

wisdom of social multimedia

tos of social apis

social networking sites