  In recent years, social networks have shown diversity in function and
applications. People begin to use multiple online social networks
simultaneously for different demands. The ability to uncover a user's latent
topic and social network preference is critical for community detection,
recommendation, and personalized service across social networks. Unfortunately,
most current works focus on the single network, necessitating new technology
and models to address this issue. This paper proposes a user preference
discovery model on multiple social networks. Firstly, the global and local
topic concepts are defined, then a latent semantic topic discovery method is
used to obtain global and local topic word distributions, along with user topic
and social network preferences. After that, the topic distribution
characteristics of different social networks are examined, as well as the
reasons why users choose one network over another to create a post. Next, a
Gibbs sampling algorithm is adopted to obtain the model parameters. In the
experiment, we collect data from Twitter, Instagram, and Tumblr websites to
build a dataset of multiple social networks. Finally, we compare our research
to previous works, and both qualitative and quantitative evaluation results
have demonstrated the effectiveness.
