Understanding User Topic Preferences across Multiple Social Networks

TL;DR

This paper introduces a model to uncover user topic and social network preferences across multiple platforms, enabling better community detection and personalized services by analyzing data from Twitter, Instagram, and Tumblr.

Contribution

It proposes a novel multi-network user preference discovery model using latent semantic topics and Gibbs sampling, addressing the gap in existing single-network focus.

Findings

Effective in identifying user preferences across platforms

Demonstrates improved community detection accuracy

Validates model with real-world social media data

Abstract

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.