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Swayed by Friends or by the Crowd?

  • Zeinab Abbassi
  • Christina Aperjis
  • Bernardo A. Huberman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7710)

Abstract

We have conducted three empirical studies of the effects of friend recommendations and general ratings on how online users make choices. We model and quantify how a user deciding between two choices trades off an additional rating star with an additional friend’s recommendation when selecting an item. We find that negative opinions from friends are more influential than positive opinions, and people exhibit “more random” behavior in their choices when the decision involves less cost and risk. Our results are quite general in the sense that people across different demographics trade off recommendations from friends and ratings from the general public in a similar fashion.

Keywords

Recommender System Online Social Network Positive Opinion Negative Opinion Average Completion Time 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Iyengar, R., Van den Bulte, C., Valente, T.W.: Opinion leadership and social contagion in new product diffusion. Marketing Science 30, 195–212 (2011)CrossRefGoogle Scholar
  2. 2.
    Aral, S., Walker, D.: Creating social contagion through viral product design: A randomized trial of peer influence in networks. In: Proceedings of the 31th Annual International Conference on Information Systems (2010)Google Scholar
  3. 3.
    Lelis, S., Howes, A.: Informing decisions: how people use online rating information to make choices. In: Proceedings of the, Annual Conference on Human Factors in Computing systems, pp. 2285–2294. ACM (2011)Google Scholar
  4. 4.
    Luca, M.: Reviews, reputation, and revenue: The case of yelp. com. Harvard Business School Working Papers (2011)Google Scholar
  5. 5.
    Sorensen, A.: Social learning and health plan choice. The RAND Journal of Economics 37(4), 929–945 (2006)CrossRefGoogle Scholar
  6. 6.
    Glaeser, E., Sacerdote, B., Scheinkman, J.: Crime and social interactions. National Bureau of Economic Research, Cambridge (1995)Google Scholar
  7. 7.
    Hullman, J., Adar, E., Shah, P.: The impact of social information on visual judgments. In: Proceedings of the 2011 Annual Conference on Human Factors in Computing Systems, pp. 1461–1470. ACM (2011)Google Scholar
  8. 8.
    Al-Hasan, A., Viswanathan, S.: The new roi return on influentials. Working Paper (2010)Google Scholar
  9. 9.
    Hong, H., Kubik, J., Stein, J.: Social interaction and stock-market participation. The Journal of Finance 59(1), 137–163 (2004)CrossRefGoogle Scholar
  10. 10.
    Tucker, C., Zhang, J.: How does popularity information affect choices? a field experiment. Management Science (forthcoming 2011)Google Scholar
  11. 11.
    Salganik, M., Dodds, P., Watts, D.: Experimental study of inequality and unpredictability in an artificial cultural market. Science 311(5762), 854 (2006)CrossRefGoogle Scholar
  12. 12.
    Guo, S., Wang, M., Leskovec, J.: The role of social networks in online shopping: information passing, price of trust, and consumer choice. In: ACM Conference on Electronic Commerce (2011)Google Scholar
  13. 13.
    Guy, I., Zwerdling, N., Carmel, D., Ronen, I., Uziel, E., Yogev, S., Ofek-Koifman, S.: Personalized recommendation of social software items based on social relations. In: RecSys, pp. 53–60 (2009)Google Scholar
  14. 14.
    Groh, G., Ehmig, C.: Recommendations in taste related domains: collaborative filtering vs. social filtering. In: GROUP, pp. 127–136 (2007)Google Scholar
  15. 15.
    Sinha, R.R., Swearingen, K.: Comparing recommendations made by online systems and friends. In: DELOS Workshop: Personalisation and Recommender Systems in Digital Libraries (2001)Google Scholar
  16. 16.
    Mason, W., Suri, S.: Conducting behavioral research on amazon’s mechanical turk. Behavior Research Methods, 1–23 (2010)Google Scholar
  17. 17.
    Kittur, A., Chi, E., Suh, B.: Crowdsourcing user studies with mechanical turk. In: Proceedings of the Twenty-Sixth Annual SIGCHI Conference on Human Factors in Computing Systems, pp. 453–456. ACM (2008)Google Scholar
  18. 18.
    Horton, J.J., Rand, D.G., Zeckhauser, R.: The online laboratory: Conducting experiments in a real labor market. Experimental Economics (2011)Google Scholar
  19. 19.
    Heer, J., Bostock, M.: Crowdsourcing graphical perception: using mechanical turk to assess visualization design. In: Proceedings of the 28th International Conference on Human Factors in Computing Systems, CHI 2010, pp. 203–212. ACM, New York (2010)Google Scholar
  20. 20.
    Heppner, P., Wampold, B., Kivlighan, D.: Research design in counseling. Brooks/Cole Pub. Co. (2008)Google Scholar
  21. 21.
    Paez, A., Scott, D., Volz, E.: A discrete-choice approach to modeling social influence on individual decision making. Environment and Planning B: Planning and Design 35(6), 1055–1069 (2008)CrossRefGoogle Scholar
  22. 22.
    Hardin, J., Hilbe, J., Hilbe, J.: Generalized linear models and extensions. Stata Corp. (2007)Google Scholar
  23. 23.
    Baumeister, R., Bratslavsky, E., Finkenauer, C., Vohs, K.: Bad is stronger than good. Review of General Psychology 5(4), 323 (2001)CrossRefGoogle Scholar
  24. 24.
    Peeters, G., Czapinski, J.: Positive-negative asymmetry in evaluations: The distinction between affective and informational negativity effects. European Review of Social Psychology 1, 33–60 (1990)CrossRefGoogle Scholar
  25. 25.
    Taylor, S.: Asymmetrical effects of positive and negative events: The mobilization-minimization hypothesis. Psychological Bulletin 110(1), 67 (1991)CrossRefGoogle Scholar
  26. 26.
    Cheung, C., Lee, M.: Online consumer reviews: Does negative electronic word-of-mouth hurt more? In: AMCIS 2008 Proceedings, p. 143 (2008)Google Scholar
  27. 27.
    Hao, Y., Ye, Q., Li, Y., Cheng, Z.: How does the valence of online consumer reviews matter in consumer decision making? differences between search goods and experience goods. In: 2010 43rd Hawaii International Conference on System Sciences (HICSS), pp. 1–10. IEEE (2010)Google Scholar
  28. 28.
    Anderson, N.: Averaging versus adding as a stimulus-combination rule in impression formation. Journal of Experimental Psychology 70(4), 394 (1965)CrossRefGoogle Scholar
  29. 29.
    Anderson: Foundations of Information Integration Theory. Academic Press, New York (1981)Google Scholar
  30. 30.
    Kahneman, D., Tversky, A.: Prospect theory: An analysis of decision under risk. Econometrica: Journal of the Econometric Society, 263–291 (1979)Google Scholar
  31. 31.
    Golbeck, J., Hendler, J.: Filmtrust: Movie recommendations using trust in web-based social networks. In: Proceedings of the IEEE Consumer Communications and Networking Conference, vol. 96. Citeseer (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Zeinab Abbassi
    • 1
  • Christina Aperjis
    • 2
  • Bernardo A. Huberman
    • 2
  1. 1.Department of Computer ScienceColumbia UniversityNew YorkUSA
  2. 2.Social Computing Group, HP LabsPalo AltoUSA

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