Friends in protest: behavioural styles, networks
and affordances of four political groups on
Facebook
Giuseppe A. Veltri (U...
Introduction: FB for research?
Actions don’t lie [Chamley 2004]
Large amounts of data with little effort
Observational: cap...
Behavioural style of minorities/majorities
In Moscovici’s theory (1984) of minority influence, one important
aspect is that...
Affordances of FB
Ever-newer waters flow on those who step into the same rivers.
[Heraclitus]
Stream moves fast
Echo chamber...
Data
Two minority groups at opposite sides:
No TAV
Casa Pound
“Baseline” comparisons: two majority groups
Partito Democrat...
Hypotheses and questions
Minority groups:
more than majority (re-)defining reality (anchoring)
recruitment
more than majori...
Sample definition
For each public page, you can download the entire stream of posts
by the page admins
by others (writing o...
Network data extraction
Data from a facebook page can be
represented as a (temporal)
two-mode network
a mode of posts
a mo...
Page activity
Two main roles of FB pages:
producer of content
relayer of content produced elsewhere
on FB: shared posts
ou...
Page activity: content
Minority groups: more photos (anchoring)
0.25
0.50
0.75
0.00/1.00
0.25
0.50
0.75
0.00/1.00
0.25
0.5...
Page activity: links and informational self-referencing
Minority groups: more links within FB
0.25
0.50
0.75
0.00/1.00
0.2...
User activity
All activities are reliable to be noticed by friends
Only recent comments on recent posts are visible to gro...
User activity: Self-boosting
Minority: more likes [Complementary Cumulative Distribution Function]
0.00
0.25
0.50
0.75
1.0...
User activity
Majority: more comments [Complementary CDF]
0.00
0.25
0.50
0.75
1.00
10 1000
ncomments [log scale]
P(X>ncomm...
User activity
Not all commenters are supporters (and vice versa)
User activity
Leftwing: more shares [Complementary CDF]
0.00
0.25
0.50
0.75
1.00
1 10 100
nshares [log scale]
P(X>nshares)...
Cost of activities: comments
[Complementary CDF]
0.00
0.25
0.50
0.75
1.00
10 1000
message_length [log scale]
P(X>message_l...
Affordances: reaction times
Most activity happens within a few hours from publication [CDF]
0.00
0.25
0.50
0.75
1.00
1s 1m ...
Affordances: reaction times
Most activity happens within a few hours from publication [CDF]
0.00
0.25
0.50
0.75
1.00
1m 1h ...
Conclusions
Minority groups:
(re-)defining reality: more anchoring
self-referencing: more content within FB + more share of...
Open issues
Sample definition (passer-by vs activist)
Fair comparisons (less posts means longer visibility)
User perception...
Thank you for your questions!
Giuseppe A. Veltri <gv35@le.ac.uk>
Matteo Gagliolo <mgagliol@ulb.ac.be>
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  1. 1. Friends in protest: behavioural styles, networks and affordances of four political groups on Facebook Giuseppe A. Veltri (University of Leicester) Matteo Gagliolo (Universit´e libre de Bruxelles) EUSN, Barcelona, July 4th, 2014
  2. 2. Introduction: FB for research? Actions don’t lie [Chamley 2004] Large amounts of data with little effort Observational: captures actual behaviour (not self-reported) ”Big” data Social dynamics Cultural evolution, opinion dynamics Issues.. Self-censoring Selection biases Affordances
  3. 3. Behavioural style of minorities/majorities In Moscovici’s theory (1984) of minority influence, one important aspect is that different behaviour styles of members that a minority group has compared to the majority. Gerard (1985) outlined the behavioural features of minority groups drawn from both theory and experimental results.
  4. 4. Affordances of FB Ever-newer waters flow on those who step into the same rivers. [Heraclitus] Stream moves fast Echo chambers (edgerank) Algorithmic gatekeeping Illusion of visibility: writing on walls with invisible ink
  5. 5. Data Two minority groups at opposite sides: No TAV Casa Pound “Baseline” comparisons: two majority groups Partito Democratico (PD) Popolo delle Libert`a (PdL) Group Posts (2012) Users No TAV 2740 38175 Casa Pound 591 17438 PD 1503 21216 PdL 3558 7075
  6. 6. Hypotheses and questions Minority groups: more than majority (re-)defining reality (anchoring) recruitment more than majority self boosting more than majority more informational social influence more than majority more self-reference behaviour Both: impact of affordances? Two main research directions: activities of the page (admins) activities of the users
  7. 7. Sample definition For each public page, you can download the entire stream of posts by the page admins by others (writing on the wall (excluded here: only NoTAV and PD allow it)) For each post: all connections likes comments (w. timestamp, likes count) shares (w. timestamp, likes, comments, shares count) . . . and all the rest (message, links, tags, pics, . . . ) For each post and connection: its author id, name, gender, language (if person) nothing else (no likes, friends, posts, . . . ) The set of all posts and their connections defines our sample of FB users (unique id’s)
  8. 8. Network data extraction Data from a facebook page can be represented as a (temporal) two-mode network a mode of posts a mode of users links connect users to post they liked post degree = n. likes on the post user degree = n. of likes by the user . . . same for comments and shares
  9. 9. Page activity Two main roles of FB pages: producer of content relayer of content produced elsewhere on FB: shared posts outside FB: external links
  10. 10. Page activity: content Minority groups: more photos (anchoring) 0.25 0.50 0.75 0.00/1.00 0.25 0.50 0.75 0.00/1.00 0.25 0.50 0.75 0.00/1.00 0.25 0.50 0.75 0.00/1.00 notav casapau pd pdl content link multimedia photo text
  11. 11. Page activity: links and informational self-referencing Minority groups: more links within FB 0.25 0.50 0.75 0.00/1.00 0.25 0.50 0.75 0.00/1.00 0.25 0.50 0.75 0.00/1.00 0.25 0.50 0.75 0.00/1.00 notav casapau pd pdl link_type External FB link No link
  12. 12. User activity All activities are reliable to be noticed by friends Only recent comments on recent posts are visible to group Activity Visibility Target Cost Impact, perceived* Impact, actual Like Public (count) Ingroup 1 click ”Count me in” Counter +1 Comment Public (count) Ingroup 1 click + text Participation, debate Counter +1 Share User set Outgroup 2 clicks (+ text) Activism, recruitment +1, Friends Post User set Ingroup 1 click + (link, text) Proposal Friends
  13. 13. User activity: Self-boosting Minority: more likes [Complementary Cumulative Distribution Function] 0.00 0.25 0.50 0.75 1.00 10 1000 nlikes [log scale] P(X>nlikes) page notav casapau pd pdl
  14. 14. User activity Majority: more comments [Complementary CDF] 0.00 0.25 0.50 0.75 1.00 10 1000 ncomments [log scale] P(X>ncomments) page notav casapau pd pdl
  15. 15. User activity Not all commenters are supporters (and vice versa)
  16. 16. User activity Leftwing: more shares [Complementary CDF] 0.00 0.25 0.50 0.75 1.00 1 10 100 nshares [log scale] P(X>nshares) page notav casapau pd pdl
  17. 17. Cost of activities: comments [Complementary CDF] 0.00 0.25 0.50 0.75 1.00 10 1000 message_length [log scale] P(X>message_length) page notav casapau pd pdl comments message_length
  18. 18. Affordances: reaction times Most activity happens within a few hours from publication [CDF] 0.00 0.25 0.50 0.75 1.00 1s 1m 1h 8h 1D 1W 1M 1Y relative_time [log scale] P(X<=relative_time) page notav casapau pd pdl comments relative_time
  19. 19. Affordances: reaction times Most activity happens within a few hours from publication [CDF] 0.00 0.25 0.50 0.75 1.00 1m 1h 8h 1D 1W 1M 1Y relative_time [log scale] P(X<=relative_time) page notav casapau pd pdl shares relative_time
  20. 20. Conclusions Minority groups: (re-)defining reality: more anchoring self-referencing: more content within FB + more share of shares within groups self boosting: more likes more cohesive? Issue: no data on friendship Impact of affordances: costlier activities are less frequent shares least frequent (low time cost, but perceived as more visible?) most activity within a few hours
  21. 21. Open issues Sample definition (passer-by vs activist) Fair comparisons (less posts means longer visibility) User perception (what do they think they’re doing?) Offline vs. offline, esp. for No TAV: how do peaks of activity relate to protest events? Network analysis proper (REM, tnet) Longer term: Questionnaires on FB: app with rights get insights on user’s perceptions, motivations get access to private data (friends, likes)
  22. 22. Thank you for your questions! Giuseppe A. Veltri <gv35@le.ac.uk> Matteo Gagliolo <mgagliol@ulb.ac.be>

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