This study used fluctuation scaling analysis on aggregate Facebook app installation data from 2007 to identify different social influence regimes. It found that some apps exhibited individual behavior with independent installations while others showed collective behavior that was highly correlated, influenced by the actions of others. However, the analysis did not have individual user or network data, relying on fluctuation scaling as a proxy, and questions remain about how subgroups, irregular usage, Facebook recommendations, and uninstalls may have impacted the results. Access to individual level social network and behavior data could have provided stronger evidence of social influence processes.

1. Spontaneous emergence of social influence in online systems(by Onnela & Reed-Tsochas) Workshop for Network AnalysisT.B. Trostli Araujo Costa

2. Agenda Research Objectives Key Concepts The Study Fluctuation Scaling Results Points of Discussion

3. Research Objectives Understand how Social Influence processes work in Online Systems Leverage the new possibilities of data from Online Systems to understand Social Influence Processes

5. The ways in which people affect each other’s beliefs, feelings and behaviours

6. Exogenous Processes

7. Processes of influence that are driven externally (outside of the network) – example: advertisement

8. Endogenous Processes

9. Processes of influence that are driven from within the network, either by:

10. Local Signals  influence within the ego network

12. The Study Focused on Facebook Applications Measured for about 2 months in 2007 Collected number of users per application per day during the timeframe (total of 104 million installations for ~2100 apps) Did NOT collect individual data (i.e. who uses each application, or who is friends with whom) Facebook Apps viewed as cultural productsor technological innovations POPULARITY OF APPS follows a fat tail, as usually seen with cultural products

14. Assumption: Facebook would only have endogenous processesSocial Networking Site allows to get more comprehensive & detailed data than offline Examples: Difficult to measure global signals in offline world for non-popular items Impossible to measure all items within a category (say: book sales)

15. Facebook ApplicationsLocal and Global Signals Local Signals – Friends Apps Global Signals – Popular Apps Status Updates

16. Method Fluctuation Scaling to understand how the behavior of individual installing an app is related to the behavior of others IMPORTANT: Did NOT download individual data (i.e. who installed which application, or who is friends with whom), therefore cannot distinguish between Global and Local signals

17. Fluctuation Scaling Logic: For an application i, the act of individual j is enclosed in a random variable Si,j(t) Si,j(t) = 1 : individual installs the application Si,j(t) = 0 : individual does nothing Probability of Si,j= 1 depends on various sources of uncertainty: Individual characteristics Application characteristics Also Global and Local Signals of Social Influence

18. Fluctuation Scaling Logic: Study measured the net activity of each application at each point in time (i.e. how many new installations happened from t0 to t1) Data of all applications (net installations, total potential new installers at each given timeframe) were analyzed The temporal average and SD of the net activity are analyzed using FS methods to identify a fluctuation scaling exponent The fluctuation scaling exponent determines the behavior of the social influence processes: 1/2 = user behavior is independent of others 1 = user behavior is fully correlated with others

19. ResultsRegimes for Apps Installation INDIVIDUAL REGIME COLLECTIVE REGIME Exponent alpha = 0.85 Strong correlation between constituent variables Influenced by the behavior of others Exponent alpha = 0.55 Installations are nearly uncorrelated Social influence is negligible

20. Additional Checks Done Could it be that groups (collective/individual) are influenced by the lifetime of the application? Cross-group checking indicated old and new applications appear in all groups Could network externalities inside app influence regime (e.g. poker versus lava lamp app)? Analyzed ~1000 apps and found both types of apps in both regimes Could regime be driven by popularity (i.e. after reaching certain # of users, app move to collective regime)? They cut the time series into pieces, recombined them using a rank-based rule, but could not find either a threshold or the existence of two regimes

21. Questions & Points of Critique Study makes assumption that FB population is uniform. Not clear whether subgroups would have their own regimes Example: An App in Dutch would not have many US users, but would it be less within the collective regime if it is not popular? Unclear how fluctuation scaling would deal with irregular Facebook usage If majority of the users don’t use it daily, how can one estimate whether irregularities in app growth is not simply due to delay (especially dealing with daily data)?

22. Questions & Points of Critique Unclear how Facebook design was dealt with Facebook already had algorithm on Apps Recommended For You. Wouldn’t this influence the Collective/Individual regime? List of popular apps assumed as Global Signal implies that list was being used by users, although no data is shown Assumption that once a user installs an app, they do not uninstall it. Unclear whether this was validated within Facebook (i.e. if the numbers communicated by FB did not remove users that uninstalled) Main Question: is this really Network Analysis, or is fluctuation scaling being used as a proxy for not gathering individual level data? If the study had access to individual level data (one’s behavior in installing applications, and one’s friends behavior), wouldn’t social influence processes be more demonstrated?