  Studying temporal dynamics of topics in social media is very useful to
understand online user behaviors. Most of the existing work on this subject
usually monitors the global trends, ignoring variation among communities. Since
users from different communities tend to have varying tastes and interests,
capturing community-level temporal change can improve the understanding and
management of social content. Additionally, it can further facilitate the
applications such as community discovery, temporal prediction and online
marketing. However, this kind of extraction becomes challenging due to the
intricate interactions between community and topic, and intractable
computational complexity.
  In this paper, we take a unified solution towards the community-level topic
dynamic extraction. A probabilistic model, CosTot (Community Specific
Topics-over-Time) is proposed to uncover the hidden topics and communities, as
well as capture community-specific temporal dynamics. Specifically, CosTot
considers text, time, and network information simultaneously, and well
discovers the interactions between community and topic over time. We then
discuss the approximate inference implementation to enable scalable computation
of model parameters, especially for large social data. Based on this, the
application layer support for multi-scale temporal analysis and community
exploration is also investigated.
  We conduct extensive experimental studies on a large real microblog dataset,
and demonstrate the superiority of proposed model on tasks of time stamp
prediction, link prediction and topic perplexity.
