Truth Discovery with Multiple Conflicting Information Providers on the Web
IEEE Transactions on Knowledge and Data Engineering
Corroborating information from disagreeing views
Proceedings of the third ACM international conference on Web search and data mining
A Bayesian approach to discovering truth from conflicting sources for data integration
Proceedings of the VLDB Endowment
Eliminating spammers and ranking annotators for crowdsourced labeling tasks
The Journal of Machine Learning Research
Besting the quiz master: crowdsourcing incremental classification games
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
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Reconciling opinions from multiple sources on questions of interest to determine the correct answers is an important problem encountered in collaborative information systems such as Q & A forums and prediction markets. Our current work focuses on a widely applicable variant of the above problem where the opinions and answers are categorical-valued with the set of values possibly varying across questions. Most of the existing techniques are tailored only for binary opinions and cannot be effectively adapted for questions with categorical opinions. To address this, we propose a generic Bayesian framework for opinion reconciliation that can readily incorporate latent and observed attributes of sources and subjects. For the scenario of interest, we derive three specific model instantiations of the general approach (CTM, CTM-OSF, CTM-LSG), which respectively capture the latent source behavior, variations of source behavior across subject groups, and inter-source correlations. Empirical results on real-world datasets point to the relative superiority of the proposed models over existing baselines.