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We study the problem of analyzing influence of various factors affectingindividual messages posted in social media. The problem is challenging becauseof various types of influences propagating through the social media networkthat act simultaneously on any user. Additionally, the topic composition of theinfluencing factors and the susceptibility of users to these influences evolveover time. This problem has not studied before, and off-the-shelf models areunsuitable for this purpose. To capture the complex interplay of these variousfactors, we propose a new non-parametric model called the DynamicMulti-Relational Chinese Restaurant Process. This accounts for the user networkfor data generation and also allows the parameters to evolve over time.Designing inference algorithms for this model suited for large scalesocial-media data is another challenge. To this end, we propose a scalable andmulti-threaded inference algorithm based on online Gibbs Sampling. Extensiveevaluations on large-scale Twitter and Facebook data show that the extractedtopics when applied to authorship and commenting prediction outperformstate-of-the-art baselines. More importantly, our model produces valuableinsights on topic trends and user personality trends, beyond the capability ofexisting approaches.

Dynamic Multi-Relational Chinese Restaurant Process for Analyzing Influences on Users in Social Media