The Author-Topic-Community model for author interest profiling and community discovery

Abstract

In this paper, we propose a generative model named the author-topic-community (ATC) model for representing a corpus of linked documents. The ATC model allows each author to be associated with a topic distribution and a community distribution as its model parameters. A learning algorithm based on variational inference is derived for the model parameter estimation where the two distributions are essentially reinforcing each other during the estimation. We compare the performance of the ATC model with two related generative models using first synthetic data sets and then real data sets, which include a research community data set, a blog data set, a news-sharing data set, and a microblogging data set. The empirical results obtained confirm that the proposed ATC model outperforms the existing models for tasks such as author interest profiling and author community discovery. We also demonstrate how the inferred ATC model can be used to characterize the roles of users/authors in online communities.

Analyzing the Changes in Online Community based on Topic Model and Self-Organizing Map

“…In [5], the authors introduce CART model (CommunityAuthor -Recipient -Topic), the model is tested on the Enron email data system 1 . The model shows that the discussion and exchange between users within a community are related to the other users in community.…”

“…1 https://www.cs.cmu.edu/~./enron/ CART model [5] is one of the first attempts to discover the community by combining research-based content message that users of community to exchange on social network. The model consists of 4 main components in CART are C, A, R and T. In particular, C is a community of users, R is the recipients, A is authors, T is topics [5].…”

“…Online community on the social network is a group of individuals who interact through specific media are able to overcome geographical boundaries and politics to pursue common interests or goals. In [5][10] [12][16] [20], community is a group of users who live and work in the same environment. One of the most popular virtual community types is social networking community.…”

“…One of the most popular virtual community types is social networking community. Figure 1 shows the structure of a social network through interacting of users in communities [5]. There are 3 communities and the links are among communities by discussed messages.…”

“…In particular, p(c|u) is the probability of community c that contains the user u [5]. Thus, each user u is contained in only the community in each period of time.…”