Knowledge sharing and yahoo answers: everyone knows something

ABSTRACT

Yahoo Answers (YA) is a large and diverse question-answer forum, acting not only as a medium for sharing technical knowledge, but as a place where one can seek advice, gather opinions, and satisfy one's curiosity about a countless number of things. In this paper, we seek to understand YA's knowledge sharing and activity. We analyze the forum categories and cluster them according to content characteristics and patterns of interaction among the users. While interactions in some categories resemble expertise sharing forums, others incorporate discussion, everyday advice, and support. With such a diversity of categories in which one can participate, we find that some users focus narrowly on specific topics, while others participate across categories. This not only allows us to map related categories, but to characterize the entropy of the users' interests. We find that lower entropy correlates with receiving higher answer ratings, but only for categories where factual expertise is primarily sought after. We combine both user attributes and answer characteristics to predict, within a given category, whether a particular answer will be chosen as the best answer by the asker.

References

1. E. Agichtein, C. Castillo, D. Donato, A. Gionis, and G. Mishne. Finding High-Quality Content in Social Media. WDSM'08, 2008.
2. J. Arguello, B. S. Butler, L. Joyce, R. Kraut, K. S. Ling, and X. Wang. Talk to me: foundations for successful individual-group interactions in online communities. In CHI'06, pages 959--968, 2006.
3. B. Butler. Membership Size, Communication Activity, and Sustainability: A Resource-Based Model of Online Social Structures. Information Systems Research, 12(4):346--362, 2001.
4. T. Davenport and L. Prusak. Working Knowledge: How Organizations Manage What They Know. Harvard Business School Press, 1998.
5. J. S. Donath. Identity and deception in the virtual community. Communities in Cyberspace, pages 29--59, 1999.
6. D. Engelbart and J. Ruilifson. Bootstrapping our collective intelligence. ACM Computing Surveys (CSUR), 31, 1999.
7. D. Fisher, M. Smith, and H. Welser. You Are Who You Talk To: Detecting Roles in Usenet Newsgroups. In HICSS'06, 2006.
8. T. Holloway, M. Bozicevic, and K. Börner. Analyzing and visualizing the semantic coverage of wikipedia and its authors: Research articles. Complexity, 12(3):30--40, 2007.
9. E. Joyce and R. Kraut. Predicting Continued Participation in Newsgroups. Journal of Computer-Mediated Communication, 11(3):723--747, 2006.
10. S. Kim, J. S. Oh, and S. Oh. Best-Answer Selection Criteria in a Social Q&A site from the User-Oriented Relevance Perspective. presented at ASIST, 2007.
11. K. Lakhani and E. von Hippel. How open source software works: "free" user-to-user assistance. Research Policy, 32(6):923--943, 2003.
12. R. Milo, S. Itzkovitz, N. Kashtan, R. Levitt, S. Shen-Orr, I. Ayzenshtat, M. Sheffer, and U. Alon. Superfamilies of evolved and designed networks. Science, 303:1538--1542, 2004.
13. R. Milo, S. Shen-Orr, S. Itzkovitz, N. Kashtan, D. Chklovskii, and U. Alon. Network Motifs: Simple Building Blocks of Complex Networks. Science, 298(5594):824--827, 2002.
14. Y. Noguchi. Web searches go low-tech: You ask, a person answers. Washington Post, page A01, 2006.
15. J. Preece, B. Nonnecke, and D. Andrews. The top five reasons for lurking: improving community experiences for everyone. Computers in Human Behavior, 20(2):201--223, 2004.
16. W. Sack. Conversation map: a content-based Usenet newsgroup browser. In IUI'00, pages 233--240, 2000.
17. Q. Su, D. Pavlov, J. Chow, and W. Baker. Internet-scale collection of human-reviewed data. In WWW'07, pages 231--240, 2007.
18. T. Turner, M. Smith, D. Fisher, and H. Welser. Picturing Usenet: Mapping Computer-Mediated Collective Action. Journal of Computer-Mediated Communication, 10(4), 2005.
19. E. Wegner. Communities of Practice: Learning, Meaning, and Identity, 1998.
20. H. T. Welser, E. Gleave, D. Fisher, and M. Smith. Visualizing the signatures of social roles in online discussion groups. Journal of Social Structure, 8(2), 2007.
21. S. Wernicke and F. Rasche. FANMOD: a tool for fast network motif detection. Bioinformatics, 22(9):1152--1153, 2006.
22. S. Whittaker, L. Terveen, W. Hill, and L. Cherny. The dynamics of mass interaction. Proceedings of the 1998 ACM conference on Computer supported cooperative work, pages 257--264, 1998.
23. J. Zhang, M. Ackerman, and L. A. Adamic. Expertise networks in online communities: structure and algorithms. In WWW'07, pages 221--230, 2007.
24. J. Zhang, M. S. Ackerman, and L. A. Adamic. Communitynetsimulator: Using simulations to study online community networks. In C & T '07, 2007.
25. K. Zhongbao and Z. Changshui. Reply networks on a bulletin board system. Phys. Rev. E, 67(3):036117, Mar 2003.

Index Terms

• Applied computing

• Human-centered computing

  • Collaborative and social computing

    • Collaborative and social computing design and evaluation methods

    • Collaborative and social computing theory, concepts and paradigms

      • Computer supported cooperative work

  • Human computer interaction (HCI)

    • Interaction paradigms

      • Web-based interaction

• Social and professional topics

  • Professional topics

    • Computing and business

      • Computer supported cooperative work