Measuring influencers in Twitter ad-hoc discussions: Active users vs. internal networks in the discourse on Biryuliovo bashings in 2013

1. Measuring influencers in Twitter ad-hoc discussions:
Active users vs. internal networks
in the discourse on Biryuliovo bashings in 2013
Saint Petersburg
State University
Svetlana S. Bodrunova
Ivan S. Blekanov
Alexey Maksimov
School of Journalism and Mass Communications
St. Petersburg State University

2. 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
Saint Petersburg
State University

3. 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?
Saint Petersburg
State University

4. 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
Saint Petersburg
State University

5. 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

6. 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

7. Research design: conflict and ‘calm’ periods
Case 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 keywords
2) Pegida + migranten +
snowballed keywords
USA Ferguson Ferguson
(+BlackLivesMatter)
France 1) charliehebdo + jesuischarlie /
jenesuispascharlie
2) AttaquesParis (+ over 20 keywords)
CharlieHebdo + migration +
snowballed keywords
AttaquesParis + migration +
snowballed keywords
Belgium AttaquesBruxelles AttaquesBruxelles + migration
+ snowballed keywords
UK Brexit (+directly related keywords) Brexit + migrants + snowballing

8. Saint Petersburg
State University
Influencers by the number of tweets

9. Saint Petersburg
State University
Influencers by various metrics: RUSSIA, Biryulyovo
Green are media, light green – Twitter media
Yellowish are the accounts of ‘angry commenters’ of oppositional stand
Orange and light-orange are nationalists and ‘patriots’
Blue are eyewitnesses, grey – spam/fake

10. Saint Petersburg
State University
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

11.
Conclusions
Saint Petersburg
State University
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.

12. Thank you
for your attention!