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1. Friends in protest: behavioural styles, networks and affordances of four political groups on Facebook Giuseppe A. Veltri (University of Leicester) Matteo Gagliolo…
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  • 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. User activity Not all commenters are supporters (and vice versa)
  • 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. 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. 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. 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. 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. 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. Thank you for your questions! Giuseppe A. Veltri <gv35@le.ac.uk> Matteo Gagliolo <mgagliol@ulb.ac.be>
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