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Studying and Modeling the Connection between People's Preferences and Content Sharing

Published:28 February 2015Publication History

ABSTRACT

People regularly share items using online social media. However, people's decisions around sharing---who shares what to whom and why---are not well understood. We present a user study involving 87 pairs of Facebook users to understand how people make their sharing decisions. We find that even when sharing to a specific individual, people's own preference for an item (individuation) dominates over the recipient's preferences (altruism). People's open-ended responses about how they share, however, indicate that they do try to personalize shares based on the recipient. To explain these contrasting results, we propose a novel process model of sharing that takes into account people's preferences and the salience of an item. We also present encouraging results for a sharing prediction model that incorporates both the senders' and the recipients' preferences. These results suggest improvements to both algorithms that support sharing in social media and to information diffusion models.

References

  1. 1. K. Abrahamson. Pinterest surpasses email for sharing online and beats Facebook growth in 2013. Retrieved from http://www.sharethis.com/blog/2014/01/16/ pinterest-surpasses-email-sharing-onlinebeats-facebook-growth-2013, January 2014.Google ScholarGoogle Scholar
  2. 2. S. Aral and D. Walker. Identifying influential and susceptible members of social networks. Science, 2012.Google ScholarGoogle Scholar
  3. 3. E. Bakshy, I. Rosenn, C. Marlow, and L. Adamic. The role of social networks in information diffusion. In Proc. WWW, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. 4. A. Barasch and J. Berger. Broadcasting and narrowcasting: How audience size impacts what people share. Journal of Marketing Research, 2014.Google ScholarGoogle ScholarCross RefCross Ref
  5. 5. M. S. Bernstein, A. Marcus, D. R. Karger, and R. C. Miller. Enhancing directed content sharing on the web. In Proc. CHI, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. 6. R. M. Bond, C. J. Fariss, J. J. Jones, A. D. Kramer, C. Marlow, J. E. Settle, and J. H. Fowler. A 61-million-person experiment in social influence and political mobilization. Nature, 2012.Google ScholarGoogle Scholar
  7. 7. J. J. Brown and P. H. Reingen. Social ties and word-of-mouth referral behavior. Journal of Consumer Research, pages 350--362, 1987.Google ScholarGoogle ScholarCross RefCross Ref
  8. 8. M. Cha, A. Mislove, and K. P. Gummadi. A measurement-driven analysis of information propagation in the flickr social network. In Proc. WWW, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. 9. C. M. Chung and P. R. Darke. The consumer as advocate: self-relevance, culture, and word-of-mouth. Marketing Letters, 17(4):269--279, 2006.Google ScholarGoogle ScholarCross RefCross Ref
  10. 10. J. Cohen. A power primer. Psychological bulletin, 1992.Google ScholarGoogle Scholar
  11. 11. E. Dichter. How word-of-mouth advertising works. Harvard Business Review, 44(6):147--160, 1966.Google ScholarGoogle Scholar
  12. 12. P. Domingos and M. Richardson. Mining the network value of customers. In Proc. KDD. ACM, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. 13. S. Goel, D. J. Watts, and D. G. Goldstein. The structure of online diffusion networks. In Proc. ACM Electron. Commerce, EC '12, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. 14. D. Gruhl, R. Guha, D. Liben-Nowell, and A. Tomkins. Information diffusion through blogspace. In Proc. WWW, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. 15. T. Hennig-Thurau and G. Walsh. Electronic word-of-mouth: Motives for and consequences of reading customer articulations on the internet. Int. J. Electron. Commerce, 8(2), Dec. 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. 16. J. Y. Ho and M. Dempsey. Viral marketing: Motivations to forward online content. J. of Business Research, 2010.Google ScholarGoogle Scholar
  17. 17. C. Johnson. Got any good recommendations? Retrieved from http://blog.netflix.com/2014/09/got-anygood-recommendations.html, September 2014.Google ScholarGoogle Scholar
  18. 18. D. Kempe, J. Kleinberg, and É. Tardos. Maximizing the spread of influence through a social network. In Proc. KDD, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. 19. R. M. Krauss and S. R. Fussell. Perspective-taking in communication: Representations of others' knowledge in reference. Social Cognition, 9(1), 1991.Google ScholarGoogle Scholar
  20. 20. V. Krishnan, P. K. Narayanashetty, M. Nathan, R. T. Davies, and J. A. Konstan. Who predicts better?: results from an online study comparing humans and an online recommender system. In Proc. RecSys, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. 21. C. Kulkarni and E. Chi. All the news that's fit to read: a study of social annotations for news reading. In Proc. CHI, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. 22. C. Lagnier, L. Denoyer, E. Gaussier, and P. Gallinari. Predicting information diffusion in social networks using content and users profiles. In Advances in Information Retrieval. Springer Berlin Heidelberg, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. 23. M. Naaman, J. Boase, and C.-H. Lai. Is it really about me? Message content in social awareness streams. In Proc. CSCW, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. 24. J. Neff. Email beats social networks for online offer sharing: Study. Retrieved from http://adage.com/article/digital/socialtwistsharing-e-mail-facebook-twitter/244397/, October 2013.Google ScholarGoogle Scholar
  25. 25. B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Item-based collaborative filtering recommendation algorithms. In Proc. WWW, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. 26. A. Sharma and D. Cosley. Network-centric recommendation: Personalization with and in social networks. In Proc. IEEE SocialCom, 2011.Google ScholarGoogle ScholarCross RefCross Ref
  27. 27. A. Sharma and D. Cosley. Do social explanations work? Studying and modeling the effects of social explanations in recommender systems. In Proc. WWW, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. 28. A. Sharma, M. Gemici, and D. Cosley. Friends, strangers, and the value of ego networks for recommendation. In Proc. ICWSM, 2013.Google ScholarGoogle Scholar
  29. 29. X. Su and T. M. Khoshgoftaar. A survey of collaborative filtering techniques. Adv. in Artif. Intell., 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. 30. D. S. Sundaram, K. Mitra, and C. Webster. Word-of-mouth communications: a motivational analysis. Advances in consumer research, 1998.Google ScholarGoogle Scholar
  31. 31. D. G. Taylor, D. Strutton, and K. Thompson. Self-enhancement as a motivation for sharing online advertising. Journal of Interactive Advertising, 2012.Google ScholarGoogle ScholarCross RefCross Ref
  32. 32. S. J. Taylor, E. Bakshy, and S. Aral. Selection effects in online sharing: Consequences for peer adoption. In Proc. ACM Electron. Commerce, EC '13, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. 33. B. Wang, C. Wang, J. Bu, C. Chen, W. V. Zhang, D. Cai, and X. He. Whom to mention: Expand the diffusion of tweets by @ recommendation on micro-blogging systems. In Proc. WWW, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. 34. M. M. Wasko and S. Faraj. Why should i share? examining social capital and knowledge contribution in electronic networks of practice. MIS Quarterly, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. 35. D. J. Watts. A simple model of global cascades on random networks. Proc. PNAS, 99(9), 2002.Google ScholarGoogle ScholarCross RefCross Ref
  36. 36. D. Zhao and M. B. Rosson. How and why people twitter: The role that micro-blogging plays in informal communication at work. In Proc. GROUP, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library

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        • Published in

          cover image ACM Conferences
          CSCW '15: Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing
          February 2015
          1956 pages
          ISBN:9781450329224
          DOI:10.1145/2675133

          Copyright © 2015 ACM

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          Publication History

          • Published: 28 February 2015

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