  In recent years, social networks have shown diversity and difference, people
begin to use multiple online social networks with the demand for different
information content at the same time. The different social networks provide
people with kinds of services, and that understanding users' topic preferences
across multiple social networks is key to community detection, recommendation,
and personalized service across social networks. This paper first divides user
topics into two types: global topics and local topics. Global topics are
abstracted based on the users' multiple social network data to reflect users'
high-level common preferences; Local topics are based on user data from single
social network, reflecting users' personalized specific preference influenced
by different social networks. On this basis, this paper integrates user
behavior data under different social networks, and proposes a user topic
preference model MSNT (Multiple Social Networks Topic model) for multiple
social networks. The model simulates the interaction process of users across
multiple social networks, and outputs users' global topic preferences and local
topic preferences synchronously. The model parameters are solved by Gibbs
sampling algorithm. This paper uses perplexity, likelihood and PMI-score to
verify model performance compared with existing works on data based on
well-known sites including Twitter, Instagram and Tumblr.
