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Measuring the effect of social connections on political activity on Facebook
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Measuring the effect of social connections on political activity on Facebook



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

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



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  • - Hello,goodaftetrnoon, weare Olli & Petrofrom the University of HelsinkiWearehere to persentsomepreliminaryresults of ourongoingstudyproject on how to utilize social data as communicationresearch, byusing SNA for example
  • - Here are the topics of our presentationWewillwalkyouthrough the introductionpartveryshortly and thenfocus on preliminaryfindings and discussionrelated to ourstudy
  • Weaimed at exploring and explainingpoliticalsupportersbehavior and politicalcommunication in FacebookWeusedFinnishpresidentialelections in 2012 as a case study, and focused on the secondround and the twofinalcandidates’ official FB supportpagesWeusedbothrelational data based on friendshipconnections and data on activites on the pages
  • So, hereare the twocandidates, mr. Pekka Haavisto and the elected, currentpresidentmr. Sauli NiinistöThe second round comprised two weeks time period before the final Election Day in FebruaryTherewas a ,assivecampaigning on Facebook and traditional media referredit to as "Haavisto-phenomenon”Weassumethat social media is an importantnpart of the campaining as itmayfosterpoliticalactivity and engagesupporters via social networks plus set the agenda for news in the traditional media
  • We collected from these two support pages all wallposts, comments, wallpost likes and comment likes for the period two weeksWe also gathered data on the individual users’ friendship connections in order to form a network of the friendship structuresThe collection was made afterwards
  • the growthrates oflikes in the upperdiagramsareprettysimilar. the cumulativegrowth is on the upperleft, and itshows a gain of 173 new likers per onehour for Haavisto and 153 for NiinistöIn overall the trend is decreasing, which is shown in the upperright in differencedtrenddiagram- The amount of posts per daywerequitesteady,about 460 hundred in Niinistö page per day and 1197 in Haavisto page
  • Ennakkoäänestys puffit: Haavistolla iso piikki (vasen yläkulma), Niinistölle aktiivisten käyttäjien piikki (oikea alakulma)Kun admin-initiatedposts on mukana, Niinistöläiset aktivoituu (mölisee yhtä paljon kuin Haavistolaiset), muuten haavistolaisten kannattajajoukko (poislukienadminin aloittamat postit) keskimäärin mölisee enemmän.Niinistössä page itse generoi aktiivisuutta/keskustelua, Haavistossa myös fanit
  • Thegraphsdepict main components of the twopagesThe main componentsaccount 90 % of the activeusersNiinistö pagehassmalleramount of activeusers and has a largerdiameter and radiusThe averagepathlength is smaller in Niinistö page, indicating the presence of morewellconnectedusersAveragedegreecentrality is higher in Haavisto page, making the overallfriendshipstructuremoreinterconnectedalsovisible in averageclusteringcoefficient, in that Niinistö pageusersaremoreprone to cliquesness

Measuring the effect of social connections on political activity on Facebook Measuring the effect of social connections on political activity on Facebook Presentation Transcript

  • Measuring the effect of socialconnections 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
  • Outline1. Introduction2. Data, methods & variables3. Research questions4. Comparing support groups5. Comparing user and admin initiated communication6. Discussion
  • Introduction
  • 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
  • Data, methods & variables
  • Data and method Data extracted post hoc from Facebook platform via its 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)
  • 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 receivedNumber of comments The number of comments posted to the wall postNumber 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 theOverall activity post.Active users Absolute number of different users activated in the post. The share of the active users of pages all likers in the wall post . Measures the wall posts abilityActivity level to engage the page likers (audience)Number of wall post likers The number of different users liking the wall postNumber of commenters The number of different users commenting on the wall postNumber of comment likers The number of different users liking commentsNumber of components Absolute number of friendship components within the postFriendship network edges The number of friendship connections within the postFriendship average component Mean of all friendship component sizes within the postsizeFriend 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 twoFriend overall degree week time frameFriend 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 otherNetwork friends percentage active usersPoster friend count Number of friends the author of the post has within all the active users of the page
  • Research questions
  •  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?
  • Comparing two support pages
  • Count of likes and posts
  • Overall activities & active users
  • 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 .1374Haavisto 57696 461744 13 7 6.804 16.01 .1129
  • Comparing admin and user initiated posts
  • Results: Overall activity (count of all activities)400000350000300000250000200000 User150000 Admin100000 50000 0 Niinistö Haavisto Niinistö Haavisto User 9435 17818 Admin 84 127
  • Regression coefficients explaining user generated activity level Number of Avg. Friendship components within centrality degree the post within the postNiinistö 1,075 0,511Haavisto 0,999 0,8All coeffiecients are highly statistically significant
  • In admin-initiated posts users’ intra-postconnedtedness is associated with bigger activity in Niinistö page
  • Discussion
  • Conclusion Niinistö HaavistoActivity More admin More user initiated initiatedStructure Cliques, wide DenseInteraction More friendship More community based (”friends based (”strangers interacting”) interacting)
  • 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
  • Thank you!olli.parviainen@helsinki.fipetro.poutanen@helsinki.fisalla.laaksonen@helsinki.fi mikael.rekola@helsinki.fi