A presentation given at Oxford Internet Institute's conference "Internet, politics and policy: big data, big challenges?", Sep. 2012.

1. Measuring the effect of social
connections on political activity on
Facebook
Internet, Politics, Policy 2012: Big Data, Big Challenges
Oxford, UK, Sep 20. 2012
Olli Parviainen, Petro Poutanen, Salla-Maaria Laaksonen & Mikael Rekola
Communications Research Centre CRC / University of Helsinki
Faculty of Social Sciences / Department of Social
Research / Media & Communication Studies www.helsinki.fi/crc

2. Outline
1. Introduction
2. Data, methods & variables
3. Research questions
4. Comparing support groups
5. Comparing user and admin initiated
communication
6. Discussion

3. Introduction

4. Basic details
 The study examines online political behavior
in Facebook
 Network analysis and statistical analysis are
used
 Case: Second round of the Finnish
presidential elections 2012
 Massive campaigning on Facebook
 Comparative study of the two supporter
populations

6. Data, methods & variables

7. Data and method
 Data extracted post hoc from Facebook platform via it's
FQL2 interface
 Collected and analyzed using C++, Perl, Graph.pm, Gephi
and SPSS
 Data comprises FB pages’ activities (wallposts, comments,
wallpost likes, comment likes) and structures (friendship
connections)
 Social network analysis (Wasserman & Faust, 1994;
Monge & Contractor, 2003) and traditional statistical
methods (time series, correlation, and regression analysis)

8. Likes The number of likes the page has in the time of the post.
Number of wall post likes The number of likes the wall post has received
Number of comments The number of comments posted to the wall post
Number of comment likes The number of likes the comments within the wall post have received
Sum of all activity (wall post likes, comments and comment likes). Measures the response for the
Overall activity
post.
Active users Absolute number of different users activated in the post.
The share of the active users of page's all likers in the wall post . Measures the wall post's ability
Activity level
to engage the page likers (audience)
Number of wall post likers The number of different users liking the wall post
Number of commenters The number of different users commenting on the wall post
Number of comment likers The number of different users liking comments
Number of components Absolute number of friendship components within the post
Friendship network edges The number of friendship connections within the post
Friendship average component
Mean of all friendship component sizes within the post
size
Friend average degree Mean of number of friends the active users of the post have each other
Mean of number of friends the active users of the post have with all the active users in the two
Friend overall degree
week time frame
Friend clustering coefficient The clustering coefficient of the friendships
The percentage of the active users of the post who have at least one friend among the other
Network friends percentage
active users
Poster friend count Number of friends the author of the post has within all the active users of the page

9. Research questions

10.  What kinds of friendship structures are typical in
large support groups (e.g. dyad, triads, bigger
cliques, communities)?
 How do people act in support pages (likes,
comment likes, comments, wall posts)?
 How activities are associated with the friendship
structures of the support pages?
 How the interaction patterns are associated with
the friendship structures of the support pages?

11. Comparing two support pages

12. Count of likes and posts

13. Overall activities & active users

14. Network structure shows more clustering on the
Niinistö page
Nodes Edges Diameter Radius Avg. path lg. Avg. degree central. Avg. clust. coeff.
Niinistö 35372 189673 18 9 4.699 10.72 .1374
Haavisto 57696 461744 13 7 6.804 16.01 .1129

15. Comparing admin and user
initiated posts

16. Results:
Overall activity (count of all activities)
400000
350000
300000
250000
200000 User
150000 Admin
100000
50000
0
Niinistö Haavisto
Niinistö Haavisto
User 9435 17818
Admin 84 127

17. Regression coefficients explaining user
generated activity level
Number of Avg. Friendship
components within centrality degree
the post within the post
Niinistö 1,075 0,511
Haavisto 0,999 0,8
All coeffiecients are highly statistically significant

18. In admin-initiated posts users’ intra-post
connedtedness is associated with bigger
activity in Niinistö page

19. Discussion

20. Conclusion
Niinistö Haavisto
Activity More admin More user initiated
initiated
Structure Cliques, wide Dense
Interaction More friendship More community
based (”friends based (”strangers
interacting”) interacting)

21. Implications and challenges
 Practical implication
 enhancing means for political campaining and public relations practice
 Scientific implications
 Gaining more (accurate) information on social behaviour in online
social networks
 Methodological contribution: SNA & statistics & large real world data
sets
 Challenges
 The platform infrastrucutre determines the activity heavily. For
example, how to identify the effects of the technology and include it
in the analysis, for example FB Edgerank?
 Content of the posts matters: combining textual content analysis with
activity and network measures is needed
 Contentual factors: external events, news media, gallups
 Privacy issues: demographic variables are difficult to incorporate

22. Thank you!
olli.parviainen@helsinki.fi
petro.poutanen@helsinki.fi
salla.laaksonen@helsinki.fi
mikael.rekola@helsinki.fi