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.
- 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 Scholar
- 2. S. Aral and D. Walker. Identifying influential and susceptible members of social networks. Science, 2012.Google Scholar
- 3. E. Bakshy, I. Rosenn, C. Marlow, and L. Adamic. The role of social networks in information diffusion. In Proc. WWW, 2012. Google ScholarDigital Library
- 4. A. Barasch and J. Berger. Broadcasting and narrowcasting: How audience size impacts what people share. Journal of Marketing Research, 2014.Google ScholarCross Ref
- 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 ScholarDigital Library
- 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 Scholar
- 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 ScholarCross Ref
- 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 ScholarDigital Library
- 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 ScholarCross Ref
- 10. J. Cohen. A power primer. Psychological bulletin, 1992.Google Scholar
- 11. E. Dichter. How word-of-mouth advertising works. Harvard Business Review, 44(6):147--160, 1966.Google Scholar
- 12. P. Domingos and M. Richardson. Mining the network value of customers. In Proc. KDD. ACM, 2001. Google ScholarDigital Library
- 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 ScholarDigital Library
- 14. D. Gruhl, R. Guha, D. Liben-Nowell, and A. Tomkins. Information diffusion through blogspace. In Proc. WWW, 2004. Google ScholarDigital Library
- 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 ScholarDigital Library
- 16. J. Y. Ho and M. Dempsey. Viral marketing: Motivations to forward online content. J. of Business Research, 2010.Google Scholar
- 17. C. Johnson. Got any good recommendations? Retrieved from http://blog.netflix.com/2014/09/got-anygood-recommendations.html, September 2014.Google Scholar
- 18. D. Kempe, J. Kleinberg, and É. Tardos. Maximizing the spread of influence through a social network. In Proc. KDD, 2003. Google ScholarDigital Library
- 19. R. M. Krauss and S. R. Fussell. Perspective-taking in communication: Representations of others' knowledge in reference. Social Cognition, 9(1), 1991.Google Scholar
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 Scholar
- 25. B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Item-based collaborative filtering recommendation algorithms. In Proc. WWW, 2001. Google ScholarDigital Library
- 26. A. Sharma and D. Cosley. Network-centric recommendation: Personalization with and in social networks. In Proc. IEEE SocialCom, 2011.Google ScholarCross Ref
- 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 ScholarDigital Library
- 28. A. Sharma, M. Gemici, and D. Cosley. Friends, strangers, and the value of ego networks for recommendation. In Proc. ICWSM, 2013.Google Scholar
- 29. X. Su and T. M. Khoshgoftaar. A survey of collaborative filtering techniques. Adv. in Artif. Intell., 2009. Google ScholarDigital Library
- 30. D. S. Sundaram, K. Mitra, and C. Webster. Word-of-mouth communications: a motivational analysis. Advances in consumer research, 1998.Google Scholar
- 31. D. G. Taylor, D. Strutton, and K. Thompson. Self-enhancement as a motivation for sharing online advertising. Journal of Interactive Advertising, 2012.Google ScholarCross Ref
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 35. D. J. Watts. A simple model of global cascades on random networks. Proc. PNAS, 99(9), 2002.Google ScholarCross Ref
- 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 ScholarDigital Library
Index Terms
- Studying and Modeling the Connection between People's Preferences and Content Sharing
Recommendations
Modeling the effect of people's preferences and social forces on adopting and sharing items
RecSys '14: Proceedings of the 8th ACM Conference on Recommender systemsRecommender systems within social networks face three distinct challenges: suggesting what to consume/adopt, what to share and who to share it with. For all three cases, my and others' research work shows that people's decisions to adopt and share ...
A (t, n) threshold quantum visual secret sharing
Secure data sharing is a deserving topic in wireless sensor networks. In order to deliver the information securely, we propose a (t, n) threshold quantum visual secret sharing (QVSS) scheme based on Naor et al.'s visual secret sharing (VSS) scheme, which ...
The Impact of Sharing Mechanism Design on Content Sharing in Online Social Networks
Research on online content diffusion is vast but has rarely examined contextual factors, including the influence of online sharing mechanisms, such as social plugins e.g., Facebook's "Like" button, on online social networks OSNs. While these mechanisms ...
Comments