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World Wide Web

, Volume 18, Issue 1, pp 33–72 | Cite as

Automatic evaluation of information provider reliability and expertise

  • Konstantinos Pelechrinis
  • Vladimir Zadorozhny
  • Velin Kounev
  • Vladimir Oleshchuk
  • Mohd Anwar
  • Yiling Lin
Article

Abstract

Q&A social media have gained a lot of attention during the recent years. People rely on these sites to obtain information due to a number of advantages they offer as compared to conventional sources of knowledge (e.g., asynchronous and convenient access). However, for the same question one may find highly contradicting answers, causing an ambiguity with respect to the correct information. This can be attributed to the presence of unreliable and/or non-expert users. These two attributes (reliability and expertise) significantly affect the quality of the answer/information provided. We present a novel approach for estimating these user’s characteristics relying on human cognitive traits. In brief, we propose each user to monitor the activity of his peers (on the basis of responses to questions asked by him) and observe their compliance with predefined cognitive models. These observations lead to local assessments that can be further fused to obtain a reliability and expertise consensus for every other user in the social network (SN). For the aggregation part we use subjective logic. To the best of our knowledge this is the first study of this kind in the context of Q&A SNs. Our proposed approach is highly distributed; each user can individually estimate the expertise and the reliability of his peers using his direct interactions with them and our framework. The online SN (OSN), which can be considered as a distributed database, performs continuous data aggregation for users expertise and reliability assesment in order to reach a consensus. In our evaluations, we first emulate a Q&A SN to examine various performance aspects of our algorithm (e.g., convergence time, responsiveness etc.). Our evaluations indicate that it can accurately assess the reliability and the expertise of a user with a small number of samples and can successfully react to the latter’s behavior change, provided that the cognitive traits hold in practice. Furthermore, the use of the consensus operator for the aggregation of multiple opinions on a specific user, reduces the uncertainty with regards to the final assessment. However, as real data obtained from Yahoo! Answers imply, the pairwise interactions between specific users are limited. Hence, we consider the aggregate set of questions as posted from the system itself and we assess the expertise and realibility of users based on their response behavior. We observe, that users have different behaviors depending on the level at which we are observing them. In particular, while their activity is focused on a few general categories, yielding them reliable, their microscopic (within general category) activity is highly scattered.

Keywords

Q&A social networks User reliability User expertise Subjective logic 

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References

  1. 1.
    Aberer, K., Despotovic, Z.: Managing trust in a peer-2-peer information system. In: ACM CIKM (2001)Google Scholar
  2. 2.
    Ackerman, M.S., McDonald, D.W.: Answer garden 2: merging organizational memory with collaborative help. In: CSCW (1996)Google Scholar
  3. 3.
    Adamic, L.A., Zhang, J., Bakshy, E., Ackerman, M.S.: Knowledge sharing and yahoo answers: everyone knows something. In: Proceeding of the 17th International Conference on World Wide Web (WWW ’08), pp. 665–674. ACM, New York (2008)CrossRefGoogle Scholar
  4. 4.
    Agichtein, E., Castillo, C., Donato, D., Gionis, A., Mishne, G.: Finding high-quality content in social media. In: Proceedings of the International Conference on Web Search and Web Data Mining (WSDM ’08), pp. 183–194. ACM, New York (2008)CrossRefGoogle Scholar
  5. 5.
    Bian, J., Liu, Y., Agichtein, E., Zha, H.: Finding the right facts in the crowd: factoid question answering over social media. In: Proceeding of the 17th International Conference on World Wide Web (WWW ’08), pp. 467–476. ACM, New York (2008)CrossRefGoogle Scholar
  6. 6.
    Bian, J., Liu, Y., Zhou, D., Agichtein, E., Zha, H.: Learning to recognize reliable users and content in social media with coupled mutual reinforcement. In: Proceedings of the 18th International Conference on World Wide Web (WWW ’09), pp. 51–60. ACM, New York (2009)CrossRefGoogle Scholar
  7. 7.
    Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: CUAI (1998)Google Scholar
  8. 8.
    Bouguessa, M., Dumoulin, B., Wang, S.: Identifying authoritative actors in question-answering forums: the case of Yahoo! Answers. In: Proceeding of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’08), pp. 866–874. ACM, New York (2008)CrossRefGoogle Scholar
  9. 9.
    Buchegger, S., Le Boudec, J.-Y.: A robust reputation system for p2p and mobile ad-hoc networks. In: P2PEcon (2004)Google Scholar
  10. 10.
    Cornelli, F., Damiani, E., Vimercati, S.D.C.D., Paraboschi, S., Samarati, S.: Choosing reputable servents in a p2p network. In: WWW (2002)Google Scholar
  11. 11.
    Dom, B., Eiron, I., Cozzi, A., Zhang, Y.: Graph-based ranking algorithms for e-mail expertise analysis. In: DMKD (2003)Google Scholar
  12. 12.
    Eysenbach, G., Kohler, C.: How do consumers search for and appraise health information on the world wide web? Qualitative study using focus groups, usability tests, and in-depth interviews. Br. Med. J. 324, 573–577 (2002)CrossRefGoogle Scholar
  13. 13.
    Foner, L.N.: Yenta: a multi-agent, referral-based matchmaking system. In: Agents (1997)Google Scholar
  14. 14.
    Ganeriwal, S., Srivastava, M.: Reputation-based framework for high integrity sensor networks. In: SASN (2004)Google Scholar
  15. 15.
    Golbeck, J., Fleischmann, K.R.: Trust in social Q&A: the impact of text and photo cues of expertise. In: Proceedings of the American Society for Information Science and Technology (ASIS&T), pp. 1–10. Wiley Subscription Services (2010)Google Scholar
  16. 16.
    Guo, J., Xu, S., Bao, S., Yu, Y.: Tapping on the potential of Q&A community by recommending answer providers. In: Proceeding of the 17th ACM Conference on Information and Knowledge Management (CIKM ’08), pp. 921–930. ACM, New York (2008)CrossRefGoogle Scholar
  17. 17.
    Hang, C.W., Wang, Y., Singh, M.P.: Operators for propagating trust and their evaluation in social networks. In: AAMAS (2009)Google Scholar
  18. 18.
    Huynh, T.D., Jennings, N.R., Shadbolt, N.R.: An integrated trust and reputation model for open multi-agent systems. Journal of Autonomous Agents and MultiAgent Systems 13, 119–154 (2006)Google Scholar
  19. 19.
    John, A., Seligmann, D.: Collaborative tagging and expertise in the enterprise. In: WWW (2006)Google Scholar
  20. 20.
    John, B.M., Chua, A.Y.K., Goh, D.H.L.: What makes a high-quality user-generated answer? IEEE Internet Computing 15(1), 66–71 (2011)CrossRefGoogle Scholar
  21. 21.
    Josang, A.: Artificial reasoning with subjective logic. In: Second Australian Workshop on Commonsense Reasoning (1997)Google Scholar
  22. 22.
    Josang, A.: A logic for uncertain probabilities. Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 9(3), 279–311 (2001)CrossRefMathSciNetGoogle Scholar
  23. 23.
    Jurczyk, P., Agichtein, E.: Discovering authorities in question answer communities by using link analysis. In: Proceedings of the Sixteenth ACM Conference on Information and Knowledge Management (CIKM ’07), pp. 919–922. ACM, New York (2007)CrossRefGoogle Scholar
  24. 24.
    Kamvar, S.D., Schlosser, M.T., Garcia-Molina, H.: The eigentrust algorithm for reputation management in p2p networks. In: WWW (2003)Google Scholar
  25. 25.
    Kasneci, G., Van Gael, J., Stern, D., Graepel, T.: Cobayes: Baysian knowledge corroboration with assessors of unknown areas of expertise. In: WSDM (2011)Google Scholar
  26. 26.
    Kautz, H., Milewski, A., Selman, B.: Agent amplified communication. In: National Conference of Artificial Intelligence (1996)Google Scholar
  27. 27.
    Kautz, H., Selman, B., Shah, M.: Referralweb: combining social networks and collaborative filtering. ACM Commun. 40(3) (1997)Google Scholar
  28. 28.
    Kotter, J.P.: Power and Influence: Beyond Formal Authority. Free Press, ISBN 0-02-918330-8 (1985)Google Scholar
  29. 29.
    Krulwich, B., Burkey, C.: The contactfinder agent: answering bulleting board questions with referrals. In: National Conference of Artificial Intelligence (1996)Google Scholar
  30. 30.
    Means, B., Toyama, Y., Murphy, R., Bakia, M., Jones, K.: Evaluation of evidence-based practices in online learning: a meta-analysis and review of online learning studies. In: U.S. Department of Education Office of Planning, Evaluation, and Policy Development Policy and Program Studies Service (2009)Google Scholar
  31. 31.
    Mundinger, J., Le Boudec, J.-Y.: Reputation in self-organized communication systems and beyond. In: Inter-Perf (2006)Google Scholar
  32. 32.
    Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: bringing order to the web. In: Stanford Digital Libraries Technologies Project (1998)Google Scholar
  33. 33.
    Pal, A., Konstan, J.A.: Expert identification in community question answering: exploring question selection bias. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management (CIKM ’10), pp. 1505–1508. ACM, New York (2010)Google Scholar
  34. 34.
    Panovich, K., Miller, R., Karger, D.: Tie strength in question & answer on social network sites. In: CSCW (2012)Google Scholar
  35. 35.
    Resnick, P., Iacovou, N., Suchak, M., Bergstorm, P., Riedl, J.: Grouplens: an open architecture for collaborative filtering of netnews. In: ACM CCSCW (1994)Google Scholar
  36. 36.
    Resnick, P., Zeckhauser, R., Friedman, E., Kuwabara, K.: Choosing reputable servents in a p2p network. In: ACM Communications (2000)Google Scholar
  37. 37.
    Richardson, M., Agrawal, R., Dominigos, P.: Trust management for the semantic web. In: ISWC (2003)Google Scholar
  38. 38.
    Sabater, J., Sierra, C.: Reputation and social network analysis in multi-agent systems. In: AAMAS (2002)Google Scholar
  39. 39.
    Shachaf, P.: Answer quality on Q&A sites. In: Proceedings of the Sixteenth Americas Conference on Information Systems (AMCIS ’10). AIS Electronic Library (2010)Google Scholar
  40. 40.
    Shah, C., Pomerantz, J.: Evaluating and predicting answer quality in community qa. In: Proceeding of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR ’10, pp. 411–418, ACM (2010)Google Scholar
  41. 41.
    Steiner, I.: Google Seller-Rating System a Threat to eBay?. http://www.ecommercebytes.com/cab/abn/y06/m08/i01/s04 (2006)
  42. 42.
    Streeter, L., Lochbaum, K.: Who knows: a system based on automatic representation of semantic structure. In: RIAO (1988)Google Scholar
  43. 43.
    Sun, Y., Yu, W., Han, Z., Ray Liu, K.J.: Information theoretic framework of trust modeling and evaluation for ad hoc networks. In: IEEE JSAC, pp. 305–317 (2006)Google Scholar
  44. 44.
    Suryanto, M.A., Lim, E.P., Sun, A., Chiang, R.H.L.: Quality-aware collaborative question answering: methods and evaluation. In: Proceedings of the Second ACM International Conference on Web Search and Data Mining (WSDM ’09), pp. 142–151. ACM, New York (2009)CrossRefGoogle Scholar
  45. 45.
    Theodorakopoulos, G., Baras, J.: On trust models and trust evaluation metrics for ad hoc networks. In: IEEE JSAC (2006)Google Scholar
  46. 46.
    Tu, X., Wang, X.-J., Feng, D., Zhang, L.: Analogical reasoning for answer ranking in social question answering. IEEE Intell. Syst. 27(5), 28–35 (2010)CrossRefGoogle Scholar
  47. 47.
    Wang, Y., Singh, M.P.: Trust via evidence combination: a mathematical approach based on uncertainty. In: TR 2006 North Carolina State University (2006)Google Scholar
  48. 48.
    Wang, Y., Singh, M.P.: Trust representation and aggregation in a distributed agent system. In: AAAI (2006)Google Scholar
  49. 49.
    Wang, Y., Singh, M.P.: Formal trust model for multiagent systems. In: IJCAI (2007)Google Scholar
  50. 50.
    Wang, G., Gill, K., Mohanlal, M., Zheng, H., Zhao, B.: Wisdom in the social crowd: an analysis of quora. In: WWWW (2013)Google Scholar
  51. 51.
    Zhang, J., Ackerman, M.A., Adamic, L.: Expertise networks in online communities: structure and algorithms. In: WWW (2007)Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Konstantinos Pelechrinis
    • 1
  • Vladimir Zadorozhny
    • 1
  • Velin Kounev
    • 1
  • Vladimir Oleshchuk
    • 2
  • Mohd Anwar
    • 3
  • Yiling Lin
    • 1
  1. 1.School of Information SciencesUniversity of PittsburghPittsburghUSA
  2. 2.Department of Information & Communication TechnologyUniversity of AgderGrimstadNorway
  3. 3.Department of Computer ScienceNC A&T State UniversityGreensboroUSA

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