Probabilistic Graphical Models for Credibility Analysis in Evolving 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.