ABSTRACT
Reputable users are valuable assets of a web site. We focus on user reputation in a comment rating environment, where users make comments about content items and rate the comments of one another. Intuitively, a reputable user posts high quality comments and is highly rated by the user community. To our surprise, we find that the quality of a comment judged editorially is almost uncorrelated with the ratings that it receives, but can be predicted using standard text features, achieving accuracy as high as the agreement between two editors! However, extracting a pure reputation signal from ratings is difficult because of data sparseness and several confounding factors in users' voting behavior. To address these issues, we propose a novel bias-smoothed tensor model and empirically show that our model significantly outperforms a number of alternatives based on Yahoo! News, Yahoo! Buzz and Epinions datasets.
- N. Agarwal, H. Liu, L. Tang, and P. S. Yu. Identifying the influential bloggers in a community. In WSDM, 2008. Google ScholarDigital Library
- E. Agichtein, C. Castillo, D. Donato, A. Gionis, and G. Mishne. Finding high-quality content in social media. Proceedings of the international conference on Web search and web data mining - WSDM '08, 2008. Google ScholarDigital Library
- E. M. Airoldi, D. M. Blei, S. E. Fienberg, and E. P. Xing. Mixed membership stochastic blockmodels. JMLR, 2008. Google ScholarDigital Library
- J. Bian, Y. Liu, D. Zhou, E. Agichtein, and H. Zha. Learning to recognize reliable users and content in social media with coupled mutual reinforcement. In WWW, 2009. Google ScholarDigital Library
- D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. JMLR, 3, 2003. Google ScholarDigital Library
- J. Booth and J. Hobert. Maximizing generalized linear mixed model likelihoods with an automated monte carlo EM algorithm. J.R. Statist. Soc. B, 1999.Google ScholarCross Ref
- A. P. Bradley. The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern Recognition, 1997. Google ScholarDigital Library
- L. Breiman. Random forests. Machine learning, 2001. Google ScholarDigital Library
- C. Danescu-niculescu mizil, J. Kleinberg, and L. Lee. How Opinions are Received by Online Communities : A Case Study on Amazon.com Helpfulness Votes. WWW, 2009. Google ScholarDigital Library
- R. Farmer and B. Glass. Building Web Reputation Systems. Yahoo Press, 2010. Google ScholarDigital Library
- R. Flesch. A new readability yardstick. Journal of Applied Psychology, 1948.Google ScholarCross Ref
- A. Ghose and P. G. Ipeirotis. Estimating the Helpfulness and Economic Impact of Product Reviews : Mining Text and Reviewer Characteristics. TKDE, 2009. Google ScholarDigital Library
- V. Gómez, A. Kaltenbrunner, and V. López. Statistical analysis of the social network and discussion threads in slashdot. In WWW, 2008. Google ScholarDigital Library
- A. Goyal, F. Bonchi, and L. V. Lakshmanan. Discovering leaders from community actions. In CIKM, 2008. Google ScholarDigital Library
- R. Guha, R. Kumar, P. Raghavan, and A. Tomkins. Propagation of trust and distrust. In WWW, 2004. Google ScholarDigital Library
- R. Gunning. The Technique of Clear Writing. 1952.Google Scholar
- R. Hellmann, J. Griesbaum, and T. Mandl. Quality in blogs: How to find the best user generated content. In Business Information Systems. 2010.Google ScholarCross Ref
- P. D. Hoff. Bilinear mixed-effects models for dyadic data. J. of the Amer. Stat. Ass., 2005.Google Scholar
- L. Hong and Z. Yang. Incorporating participant reputation in community-driven question answering systems. In Symposium on Social Intelligence and Networking, 2009. Google ScholarDigital Library
- C.-F. Hsu, E. Khabiri, and J. Caverlee. Ranking comments on the social web. In International Conference on Computational Science and Engineering, 2009. Google ScholarDigital Library
- P. G. Ipeirotis, F. Provost, and J. Wang. Quality Management on Amazon Mechanical Turk. KDD The Human Computation Workshop, 2010. Google ScholarDigital Library
- T. S. Jaakkola and M. I. Jordan. Bayesian parameter estimation via variational methods. Statistics and Computing, 2000. Google ScholarDigital Library
- Jianshu Weng, et al. Twitterrank: finding topic-sensitive influential twitterers. In WSDM, 2010. Google ScholarDigital Library
- A. Jøsang, R. Ismail, and C. Boyd. A survey of trust and reputation systems for online service provision. Decision Support Systems, 2007. Google ScholarDigital Library
- Y. Koren. Collaborative filtering with temporal dynamics. Commun. ACM, 2010. Google ScholarDigital Library
- J. Kunegis, A. Lommatzsch, and C. Bauckhage. The slashdot zoo: Mining a social network with negative edges. In WWW, 2009. Google ScholarDigital Library
- U. Kuter. Sunny: A new algorithm for trust inference in social networks using probabilistic confidence models. In AAAI, 2007. Google ScholarDigital Library
- G. H. M. C. Laughlin. SMOG Grading ŮÜ a New Readability Formula. Journal Of Reading, 1969.Google Scholar
- J. Liu, et al. Low-Quality Product Review Detection in Opinion Summarization. EMNLP-CoNLL, 2007.Google Scholar
- Y. Lu and P. Tsaparas, et al. Exploiting social context for review quality prediction. In WWW, 2010. Google ScholarDigital Library
- Y. Maeno. Node discovery in a networked organization. In Intl Conf on Sys., Man and Cyber., 2009. Google ScholarDigital Library
- P. Massa and P. Avesani. Trust metrics in recommender systems. In Computing with Social Trust, 2009.Google ScholarCross Ref
- M. G. Noll, et al. Telling experts from spammers: Expertise ranking in folksonomies. In SIGIR, 2009. Google ScholarDigital Library
- V. C. Raykar, et al. Learning From crowds. JMLR, 2010. Google ScholarDigital Library
- P. Resnick and Z. Richard. Trust Among Strangers in Internet Transactions: Empirical Analysis of eBay's Reputation System. Elsevier Science, 2002.Google ScholarCross Ref
- R. Salakhutdinov and A. Mnih. Probabilistic matrix factorization. In NIPS, 2008.Google ScholarDigital Library
- J. Schneider, et al. Disseminating trust information in wearable communities. In HUC, 2000.Google ScholarCross Ref
- H. Shin, et al. Separating the reputation and the sociability of online community users. In SAC, 2010. Google ScholarDigital Library
- A. Talwar, R. Jurca, and B. Faltings. Understanding user behavior in online feedback reporting. In EC, 2007. Google ScholarDigital Library
- S. Valenti, F. Neri, and A. Cucchiarelli. An Overview of Current Research on Automated Essay Grading. Journal of Information Technology Education, 2003.Google Scholar
- P. Welinder, S. Branson, S. Belongie, and P. Perona. The Multidimensional Wisdom of Crowds. NIPS, 2010.Google ScholarDigital Library
- P. Windley, K. Tew, and D. Daley. A framework for building reputation systems. In WWW, 2007.Google Scholar
- J. Zhang, et al. Expertise networks in online communities: structure and algorithms. In WWW, 2007. Google ScholarDigital Library
- Z. Zheng, H. Zha, K. Chen, and G. Sun. A regression framework for learning ranking functions using relative relevance judgments. In SIGIR, 2007. Google ScholarDigital Library
- C.-N. Ziegler and G. Lausen. Propagation models for trust and distrust in social networks. Info. Sys. Frontiers, 2005. Google ScholarDigital Library
Index Terms
- User reputation in a comment rating environment
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