ABSTRACT
To find interesting, personally relevant web content, people rely on friends and colleagues to pass links along as they encounter them. In this paper, we study and augment link-sharing via e-mail, the most popular means of sharing web content today. Armed with survey data indicating that active sharers of novel web content are often those that actively seek it out, we developed FeedMe, a plug-in for Google Reader that makes directed sharing of content a more salient part of the user experience. FeedMe recommends friends who may be interested in seeing content that the user is viewing, provides information on what the recipient has seen and how many emails they have received recently, and gives recipients the opportunity to provide lightweight feedback when they appreciate shared content. FeedMe introduces a novel design space within mixed-initiative social recommenders: friends who know the user voluntarily vet the material on the user's behalf. We performed a two-week field experiment (N=60) and found that FeedMe made it easier and more enjoyable to share content that recipients appreciated and would not have found otherwise.
- Ackerman, M., Starr, B., and Pazzani, M. The do-i-care agent: Effective social discovery and filtering on the web. Proc. RIAO '97, (1997), 17--31.Google Scholar
- Allen, T. Managing the Flow of Technology. MIT Press, Cambridge, MA, 1977.Google Scholar
- Baumer, E. and Fisher, D. Smarter Blogroll: An Exploration of Social Topic Extraction for Manageable Blogrolls. Proc. HICSS '08, IEEE (2008), 155. Google ScholarDigital Library
- Baumer, E., Sueyoshi, M., and Tomlinson, B. Exploring the role of the reader in the activity of blogging. Proc. CHI '08, ACM Press (2008), 1111--1120. Google ScholarDigital Library
- Bernstein, M., Tan, D., Smith, G., et al. Collabio: A Game for Annotating People within Social Networks. Proc. UIST '09, ACM Press (2009), 177--180. Google ScholarDigital Library
- Burke, M., Marlow, C., and Lento, T. Feed me: motivating newcomer contribution in social network sites. Proc. CHI '09, ACM Press (2009), 945--954. Google ScholarDigital Library
- Dennis, B. and Jarrett, A. NusEye: Visualizing Network Structure to Support Navigation of Aggregated Content. Proc. HICCS '05, IEEE Press (2005), 107c. Google ScholarDigital Library
- Ehrlich, K. and Cash, D. Turning Information into Knowledge: Information Finding as a Collaborative Activity. Proc. JCDL '94, (1994).Google Scholar
- Ellison, N.B., Steinfield, C., and Lampe, C. The benefits of Facebook "friends:" Social capital and college students' use of online social network sites. JCMC 12, 4 (2007).Google ScholarCross Ref
- Erdelez, S. and Rioux, K. Sharing information encountered for others on the Web. The New Review of Information Behaviour Research 1, (2000), 219--233. Google ScholarDigital Library
- Erickson, T. and Kellogg, W.A. Social translucence: an approach to designing systems that support social processes. TOCHI 7, 1 (2000), 59--83. Google ScholarDigital Library
- Gamon, M., Basu, S., Belenko, D., et al. Blews: Using blogs to provide context for news articles. Proc. ICWSM '08, (2008), 60--67.Google Scholar
- Granovetter, M. The Strength of Weak Ties. American Journal of Sociology 78, 6 (1973), 1360--1380.Google ScholarCross Ref
- Hearst, M. Search User Interfaces. Cambridge University Press, 2009. Google ScholarDigital Library
- Joachims, T., Freitag, D., and Mitchell, T. Webwatcher: A tour guide for the world wide web. Proc. IJCAI, AAAI (1997), 770--777.Google Scholar
- Kittur, A., Chi, E., and Suh, B. Crowdsourcing user studies with Mechanical Turk. Proc. CHI '08, ACM Press (2008), 453--456. Google ScholarDigital Library
- Marshall, C. and Bly, S. Sharing encountered information: digital libraries get a social life. Proc. JCDL '04, (2004), 218--227. Google ScholarDigital Library
- Mason, W. and Watts, D. Financial Incentives and the "Performance of Crowds". Proc. HCOMP '09, (2009), 77--85. Google ScholarDigital Library
- McDonald, D. and Ackerman, M. Expertise recommender: a flexible recommendation system and architecture. Proc. CSCW '00, ACM Press (2000), 231--240. Google ScholarDigital Library
- Montaner, M., López, B., and La, J.L. A Taxonomy of Recommender Agents on the Internet. Artificial Intelligence Review 19, 4 (2003), 285--330. Google ScholarDigital Library
- Paepcke, A. Information Needs in Technical Work Settings and Their Implications for the Design of Computer Tools. Computer Supported Cooperative Work 5, 1 (1996), 63--92. Google ScholarDigital Library
- Resnick, P., Iacovou, N., Suchak, M., et al. GroupLens: An open architecture for collaborative filtering of net-news. Proc. CSCW '94, ACM Press (1994), 175--186. Google ScholarDigital Library
- Rocchio, J. Relevance feedback in information retrieval. In G. Salton, The SMART retrieval system: experiments in automatic document processing. Prentice Hall, Englewood Cliffs, NJ, 1971, 313--323.Google Scholar
- Salton, G. and Buckley, C. Term-weighting approaches in automatic text retrieval. Information Processing and Management 24, 5 (1988), 513--523. Google ScholarDigital Library
- Sen, S., Geyer, W., Muller, M., et al. FeedMe: a collaborative alert filtering system. Proc. CSCW '06, ACM Press (2006), 89--98. Google ScholarDigital Library
- Terveen, L. and McDonald, D. Social matching: A framework and research agenda. TOCHI 12, 3 (2005), 401--434. Google ScholarDigital Library
- Terveen, L., Hill, W., Amento, B., et al. PHOAKS: A system for sharing recommendations. CACM 40, 3 (1997), 59--62. Google ScholarDigital Library
Index Terms
- Enhancing directed content sharing on the web
Recommendations
Directed social queries with transparent user models
UIST Adjunct Proceedings '12: Adjunct proceedings of the 25th annual ACM symposium on User interface software and technologyThe friend list of many social network users can be very large. This creates challenges when users seek to direct their social interactions to friends that share a particular interest. We present a self-organizing online tool that by incorporating ideas ...
Consuming, sharing, and creating content
This survey study investigated 186 secondary 25 school students from two schools to understand how and why they used new social media both in and outside of school to consume, share, and create content. It found that whereas students tend to consume and ...
On effective sharing of user generated content
APCHI '13: Proceedings of the 11th Asia Pacific Conference on Computer Human InteractionSharing of content is an important part of growing social networking culture. We examine the effectiveness of shared user-generated content (UGC) on social networking sites (SNSs). We divide the shared content into two categories: direct share, where ...
Comments