Advertisement

Peer Influence in Large Dynamic Network: Quasi-experimental Evidence from Scratch

  • Abhishek Samantray
  • Massimo Riccaboni
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 813)

Abstract

We analyze peer influence of production and consumption of projects in the Scratch community, an online platform developed by MIT Media Lab, where users collectively learn to program by creating and sharing projects. Scratchers can follow others’ activities on the platform; in the followers network, we investigate if Scratchers’ production popularity (determined by others) and consumption preference (self determined) are influenced by whom they follow on the platform (peers). Several mechanisms established in the literature like homophily, selection, peer influence, own behavioural tendency, reciprocated ties, and particular contexts can lead to observations of behavioural clustering in a social network like Scratch, and therefore isolating peer influence from other mechanisms is a challenging task. In this study, we measure peer influence in the Scratch community after accounting for such alternative confounding mechanisms. There are two key steps we follow to estimate peer influence of a behaviour. First, at a given time, we create experimental and control groups such that the peers’ behaviour under investigation can be justified as a random assignment. To do so we exactly match Scratchers’ personal and network attributes in both groups such that Scratchers in the experimental group have peers with higher degree of the behaviour under study compared to the control group, and all other attributes of Scratchers are balanced across both groups. Second, conditional on all activities up to this time (as captured by the attributes), we measure peer influence as the difference in Scratchers’ personal behavioural changes in subsequent periods across the two groups.

Keywords

Peer influence Causal mechanism Dynamic social network 

References

  1. 1.
    Aiello, L.M., Barrat, A., Schifanella, R., Cattuto, C., Markines, B., Menczer, F.: Friendship prediction and homophily in social media. ACM Trans. Web 6(2), 9:1–9: 33 (2012)CrossRefGoogle Scholar
  2. 2.
    Aral, S., Muchnik, L., Sundararajan, A.: Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks. PNAS 106(51), 21544–21549 (2009)CrossRefGoogle Scholar
  3. 3.
    Aral, S., Walker, D.: Tie strength, embeddedness, and social influence: a large-scale networked experiment. Manag. Sci. 60(6), 1352–1370 (2014)CrossRefGoogle Scholar
  4. 4.
    Atkinson, M.D., Fowler, A.: Social capital and voter turnout: evidence from saint’s day fiestas in mexico. Br. J. Polit. Sci. 44(1), 41–59 (2014)CrossRefGoogle Scholar
  5. 5.
    Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 2008(10), P10008 (2008)CrossRefGoogle Scholar
  6. 6.
    Centola, D.: The spread of behavior in an online social network experiment. Science 329(5996), 1194–1197 (2010)CrossRefGoogle Scholar
  7. 7.
    Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70(6), 066,111 (2004)CrossRefGoogle Scholar
  8. 8.
    Eckles, D.: Identifying peer influence effects in observational social network data: an evaluation of propensity score methods. Technical Report, Stanford University (2010)Google Scholar
  9. 9.
    Eckles, D., Bakshy, E.: Bias and high-dimensional adjustment in observational studies of peer effects. Technical Report, MIT (2010)Google Scholar
  10. 10.
    Feld, J., Zölitz, : U.: Understanding peer effects - on the nature, estimation and channels of peer effects. Technical Report No ROA-RM-2016/1. University, Maastricht (2016)Google Scholar
  11. 11.
    Hallinan, M.T., Williams, R.A.: Students’ characteristics and the peer-influence process. Sociol. Educ. 63(2), 122–132 (1990)CrossRefGoogle Scholar
  12. 12.
    Hill, B.M., Monroy-Hernández, A.: A longitudinal dataset of five years of public activity in the scratch online community. Sci. Data 4, 170002 (2017)CrossRefGoogle Scholar
  13. 13.
    Ho, D., Imai, K., King, G., Stuart, E.: Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference. Polit. Anal. 15(3), 199–236 (2007)CrossRefGoogle Scholar
  14. 14.
    Huckfeldt, R., Sprague, J.: Networks in context: the social flow of political information. Am. Polit. Sci. Rev. 81(4), 1197–1216 (1987)CrossRefGoogle Scholar
  15. 15.
    Imai, K., Keele, L., Tingley, D.: A general approach to causal mediation analysis. Psychol. Methods 15(4), 309–334 (2010)CrossRefGoogle Scholar
  16. 16.
    King, G., Nielsen, R.: Why propensity scores should not be used for matching. Technical Report, Harvard University (2016)Google Scholar
  17. 17.
    Kramer, A.D.I., Guillory, J.E., Hancock, J.T.: Experimental evidence of massive-scale emotional contagion through social networks. PNAS 111(24), 8788–8790 (2014)CrossRefGoogle Scholar
  18. 18.
    Lewis, K., Gonzalez, M., Kaufman, J.: Social selection and peer influence in an online social network. PNAS 109(1), 68–72 (2012)CrossRefGoogle Scholar
  19. 19.
    Manski, C.F.: Identification of endogenous social effects: the reflection problem. Rev. Econ. Stud. 60(3), 531–542 (1993)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Mason, W., Watts, D.J.: Collaborative learning in networks. PNAS 109(3), 764–769 (2012)CrossRefGoogle Scholar
  21. 21.
    McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a feather: homophily in social networks. Annu. Rev. Sociol. 27(1), 415–444 (2001)CrossRefGoogle Scholar
  22. 22.
    Newman, M.E.J.: Mixing patterns in networks. Phys. Rev. E 67(2), 026,126 (2003)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Reihaneh, R., Elatia, S., Takaffoli, M., Zaïane, O.R.: Collaborative learning of students in online discussion forums: a social network analysis perspective. Educational Data Mining: Applications and Trends, pp. 441–466. Springer, Cham (2014)Google Scholar
  24. 24.
    Sacerdote, B.: Peer effects with random assignment: results for dartmouth roommates. Q. J. Econ. 116(2), 681–704 (2001)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Shalizi, C.R., Thomas, A.C.: Homophily and contagion are generically confounded in observational social network studies. Sociol. Methods Res. 40(2), 211–239 (2011)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Snijders, T.A.: Statistical models for social networks. Annu. Rev. Sociol. 11(37), 131–53 (2011)CrossRefGoogle Scholar
  27. 27.
    Snijders, T.A., van de Bunt, G.G., Steglich, C.E.: Introduction to stochastic actor-based models for network dynamics. Soc. Netw. 32(1), 44–60 (2010)CrossRefGoogle Scholar
  28. 28.
    Steglich, C., Snijders, T.A.B., Pearson, M.: Dynamic networks and behavior: separating selection from influence. Sociol. Methodol. 40(1), 329–393 (2010)CrossRefGoogle Scholar
  29. 29.
    Tingley, D., Yamamoto, T., Hirose, K., Keele, L., Imai, K.: Mediation: R package for causal mediation analysis. J. Stat. Softw., Articles 59(5), 1–38 (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.IMT School for Advanced Studies LuccaLuccaItaly

Personalised recommendations