Enhancing directed content sharing on the web
Author(s)Bernstein, Michael S.; Marcus, Adam; Karger, David R.; Miller, Robert C.
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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.
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Proceedings of the 28th International Conference on Human Factors in Computing Systems (CHI '10)
Association for Computing Machinery
Bernstein, Michael S. et al. “Enhancing directed content sharing on the web.” Proceedings of the 28th international conference on Human factors in computing systems. Atlanta, Georgia, USA: ACM, 2010. 971-980.
Author's final manuscript