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Network Analysis Workshop - Review of Onnela & Reed-Tsochas (2010)

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Network Analysis Workshop - Review of Onnela & Reed-Tsochas (2010)

  1. 1. Spontaneous emergence of social influence in online systems(by Onnela & Reed-Tsochas)<br />Workshop for Network AnalysisT.B. Trostli Araujo Costa<br />
  2. 2. Agenda<br />Research Objectives<br />Key Concepts<br />The Study<br />Fluctuation Scaling<br />Results<br />Points of Discussion<br />
  3. 3. Research Objectives<br />Understand how Social Influence processes work in Online Systems<br />Leverage the new possibilities of data from Online Systems to understand Social Influence Processes<br />
  4. 4. Key Concepts<br /><ul><li>Social Influence:
  5. 5. The ways in which people affect each other’s beliefs, feelings and behaviours
  6. 6. Exogenous Processes
  7. 7. Processes of influence that are driven externally (outside of the network) – example: advertisement
  8. 8. Endogenous Processes
  9. 9. Processes of influence that are driven from within the network, either by:
  10. 10. Local Signals  influence within the ego network
  11. 11. Global Signals  influence from the aggregate behaviour of the population </li></li></ul><li>Dynamics of Influence<br />Driven by contacts within ego network<br />Endogenous Processes<br />Driven externally, bypassing local or global signals. <br />Aggregate info on population behavior<br />
  12. 12. The Study<br />Focused on Facebook Applications<br />Measured for about 2 months in 2007<br />Collected number of users per application per day during the timeframe (total of 104 million installations for ~2100 apps)<br />Did NOT collect individual data (i.e. who uses each application, or who is friends with whom)<br />Facebook Apps viewed as cultural productsor technological innovations<br />POPULARITY OF APPS<br />follows a fat tail, as usually seen with cultural products<br />
  13. 13. Why study Social Influence on Facebook?<br />Endogenous versus exogenous factors<br />Sampling and popularity<br /><ul><li>Processes in offline world are difficult to measure due to exogenous factors
  14. 14. Assumption: Facebook would only have endogenous processes</li></ul>Social Networking Site allows to get more comprehensive & detailed data than offline<br />Examples:<br />Difficult to measure global signals in offline world for non-popular items<br />Impossible to measure all items within a category (say: book sales)<br />
  15. 15. Facebook ApplicationsLocal and Global Signals<br />Local Signals – Friends Apps<br />Global Signals – Popular Apps<br />Status Updates<br />
  16. 16. Method<br />Fluctuation Scaling to understand how the behavior of individual installing an app is related to the behavior of others <br />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<br />
  17. 17. Fluctuation Scaling<br />Logic:<br />For an application i, the act of individual j is enclosed in a random variable Si,j(t)<br />Si,j(t) = 1 : individual installs the application<br />Si,j(t) = 0 : individual does nothing<br />Probability of Si,j= 1 depends on various sources of uncertainty:<br />Individual characteristics<br />Application characteristics<br />Also Global and Local Signals of Social Influence<br />
  18. 18. Fluctuation Scaling<br />Logic:<br />Study measured the net activity of each application at each point in time (i.e. how many new installations happened from t0 to t1)<br />Data of all applications (net installations, total potential new installers at each given timeframe) were analyzed <br />The temporal average and SD of the net activity are analyzed using FS methods to identify a fluctuation scaling exponent <br />The fluctuation scaling exponent determines the behavior of the social influence processes:<br />1/2 = user behavior is independent of others<br />1 = user behavior is fully correlated with others<br />
  19. 19. ResultsRegimes for Apps Installation<br />INDIVIDUAL REGIME<br />COLLECTIVE REGIME<br />Exponent alpha = 0.85<br />Strong correlation between constituent variables<br />Influenced by the behavior of others<br />Exponent alpha = 0.55<br />Installations are nearly uncorrelated<br />Social influence is negligible<br />
  20. 20. Additional Checks Done<br />Could it be that groups (collective/individual) are influenced by the lifetime of the application?<br />Cross-group checking indicated old and new applications appear in all groups<br />Could network externalities inside app influence regime (e.g. poker versus lava lamp app)?<br />Analyzed ~1000 apps and found both types of apps in both regimes<br />Could regime be driven by popularity (i.e. after reaching certain # of users, app move to collective regime)?<br />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<br />
  21. 21. Questions & Points of Critique<br />Study makes assumption that FB population is uniform. Not clear whether subgroups would have their own regimes<br />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?<br />Unclear how fluctuation scaling would deal with irregular Facebook usage<br />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)?<br />
  22. 22. Questions & Points of Critique<br />Unclear how Facebook design was dealt with<br />Facebook already had algorithm on Apps Recommended For You. Wouldn’t this influence the Collective/Individual regime?<br />List of popular apps assumed as Global Signal implies that list was being used by users, although no data is shown<br />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)<br />Main Question: is this really Network Analysis, or is fluctuation scaling being used as a proxy for not gathering individual level data?<br />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?<br />

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