Home location profiling for users in social media

• Study the problem of profiling “home locations” for Twitter users.
• Leverage STFG to illustrate relationship attribute information in home location prediction.
• Identify the potential set of locations by performing similarity retrieval on various features.
• STFG is more powerful than the current methods.

In this paper, we focus on the problem of estimating the home locations of users in the Twitter network. We propose a Social Tie Factor Graph (STFG) model to estimate a Twitter user's city-level location based on the user's following network, user-centric data, and tie strength. In STFG, relationships between users and locations are modeled as nodes, while attributes and correlations are modeled as factors. An efficient algorithm is proposed to learn model parameters and predict unknown relationships. We evaluate our proposed method by investigating Twitter networks. The experimental results demonstrate that our proposed method significantly outperforms several state-of-the-art methods.