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One of the major hurdles preventing the full exploitation of information from online communities is the widespread concern regarding the quality and credibility of user-contributed content. We propose probabilistic graphical models that can leverage the joint interplay between multiple factors --- like user interactions, community dynamics, and textual content --- to automatically assess the credibility of user-contributed online information, expertise of users and their evolution with user-interpretable explanation. We devise new models based on Conditional Random Fields that enable applications such as extracting reliable side-effects of drugs from user-contributed posts in health forums, and identifying credible news articles in news forums.
Online communities are dynamic, as users join and leave, adapt to evolving trends, and mature over time. To capture this dynamics, we propose generative models based on Hidden Markov Model, Latent Dirichlet Allocation, and Brownian Motion to trace the continuous evolution of user expertise and their language model over time. This allows us to identify expert users and credible content jointly over time, improving state-of-the-art recommender systems by explicitly considering the maturity of users. This enables applications such as identifying useful product reviews, and detecting fake and anomalous reviews with limited information.