ainl 2016: bodrunova, blekanov, maksimov

Measuring influencers in Twitter ad-hoc discussions: Active users vs. internal networks

in the discourse on Biryuliovo bashings in 2013

Saint PetersburgState University

Svetlana S. BodrunovaIvan S. Blekanov

Alexey MaksimovSchool of Journalism and Mass Communications

St. Petersburg State University

Research premises: societal cleavages in public discussions

1. Public discussion is highly uneven in regard to:

- who speaks (institutional players and media are privileged)- who is listened to (again, distorted by media)

2. Media-constructed public sphere:

- media as ‘junctions’ of the public sphere - media as gatekeepers of public agendas

3. Network(ed) public discussions on conflicts:

- ad-hoc publics | short-term vs. long-term discussions- tracing discussion structure | ‘echo chambers’ vs. crossroads- defining influencers | known disparities + new divisions


Research premises: societal cleavages in public discussions

Question 1: What are structural conditions for us to speak of an inclusive Twitter discussion?

What is an inclusive Twitter discussion?

Question 2: Do we see any universal patterns in the structure and influencers in similar discussions across cultures?

How do we compare?


Twitter as a communicative milieu: optimism vs. pessimism

• OPTIMISTS: Twitter as the milieu of platform-limited horizontal communication with a big news alerts potential

• PESSIMISTS: Twitter as a de-politicized space for gaming, dating, and chats (Fuchs 2014)

• STRUCTURE OF DISCUSSIONS is disputable in terms of what it is and what it tells when we deal with SNA-traced ad-hoc publics (Goncalez-Bailon; Bruns&Burgess 2011; Papacharizzi 2015; Bruns&Highfield 2016)

• INFLUENCERS are viewed as key structural elements of power and impact distribution in networked discussions. Aspects like dynamics of influencer status, its linkage to user trust (Liu et al. 2015), discussion topicality (Kelly et al. 2012) etc. are discussed, BUT there is no shared understanding of what an influencer actually is


Detecting influencers

‘Absolute’ figures SNA metrics

Activity metrics

Number of original user posts within the discussion (Ntweets)

Number of users involved into the discussion by a given user, that is, commented or retweeted by a given user (In-Degree)

Connectivity metrics

Number of received interactions, that is, retweets and comments combined, by the given user (Nrecom)

1. Number of users involving the given user into the discussion by commenting or retweeting his/her tweets (Out-Degree). 2. Betweenness centrality (BC). 3. Pagerank centrality (PR).

• Three types of divisions in literature: - ‘marketing’ vs. ‘deliberative’ influencers: conceptual difference- measured based on activity vs. based on connectivity- measured by ‘absolute’ figures vs. by SNA metrics

Research design: cases and their comparability

We look at inter-ethnic (inter-national / inter-racial) conflicts. Each case:

- has a violent component (killing / terrorist attack / harassment) - has a ‘majority / minority’ component - is an ‘iceberg tip’ of a wider societal cleavage

Cases include:

-RUSSIA: Biryuliovo bashings -GERMANY: PEGIDA demonstrations, attacks in Koln-USA: Ferguson riots -FRANCE: attack to Charlie Hebdo, Paris attacks-BELGIUM: Brussels attacks-UK: Brexit and the migration issues in it

Research design: conflict and ‘calm’ periodsCase Conflict period ‘Calm’ period

Russia Biryulyovo warehouse migrants [killer’s name] people’s gathering

Biryulyovo + migranty + snowballed keywords

Germany 1) Pegida

2) Koln pegida nopegida+ more than 10 directly related keywords

1) Pegida + migranten + snowballed keywords2) Pegida + migranten +snowballed keywords

USA Ferguson Ferguson (+BlackLivesMatter)

France 1) charliehebdo + jesuischarlie / jenesuispascharlie

2) AttaquesParis (+ over 20 keywords)

CharlieHebdo + migration + snowballed keywordsAttaquesParis + migration +snowballed keywords

Belgium AttaquesBruxelles AttaquesBruxelles + migration + snowballed keywords

UK Brexit (+directly related keywords) Brexit + migrants + snowballing


Influencers by the number of tweets


Influencers by various metrics: RUSSIA, Biryulyovo

Green are media, light green – Twitter mediaYellowish are the accounts of ‘angry commenters’ of oppositional standOrange and light-orange are nationalists and ‘patriots’Blue are eyewitnesses, grey – spam/fake


Influencer strategies: RUSSIA, Biryulyovo

In-Degree, not number of received comments, leads to high betweenness and pagerank centralities: a network of users who comment and retweet each other. In the end, they start shaping the discussion

Conclusions


1. Direct comparisons of web graphs and influencer lists do not make much sense; but we could compare is to what extent absolute metrics correlate to SNA metrics – to search for more universal activity-to-connectivity patterns.

2. So far, analysis of top posting users in calm and conflict periods shows that national patterns, quite expectedly, dominate…

3. …except for the fact that fake accounts are created in both Russia and Germany to promote nationalistic content.

4. In case of Russia, number of received comments / retweets does not seem to lead to becoming a discussion center by SNA metrics, unlike in previous research.

5. ‘Marketing’ and ‘deliberative’ influencers co-exist – those who post and get a lot of comments are not those who form a high-pagerank network. The latter may have group impact, rather than individual influence.

6. Understandably, a strategy for becoming a high-betweenness user and high-pagerank user seems the same – posting a lot and commenting various users.

Thank you for your attention!

ainl 2016: bodrunova, blekanov, maksimov

Science