Locate Who You Are: Matching Geo-location to Text for User Identity Linkage

TL;DR

This paper introduces a novel framework for matching user identities across social networks by correlating geo-locations and text content, addressing asymmetric information challenges and improving prediction accuracy.

Contribution

It proposes a new anchor link prediction method leveraging geo-location and text correlation, surpassing existing symmetric feature-based approaches and incorporating external data to reduce label scarcity.

Findings

Outperforms existing methods on real-world datasets

Achieves state-of-the-art accuracy in anchor link prediction

Effectively handles asymmetric information in user data

Abstract

Nowadays, users are encouraged to activate across multiple online social networks simultaneously. Anchor link prediction, which aims to reveal the correspondence among different accounts of the same user across networks, has been regarded as a fundamental problem for user profiling, marketing, cybersecurity, and recommendation. Existing methods mainly address the prediction problem by utilizing profile, content, or structural features of users in symmetric ways. However, encouraged by online services, users would also post asymmetric information across networks, such as geo-locations and texts. It leads to an emerged challenge in aligning users with asymmetric information across networks. Instead of similarity evaluation applied in previous works, we formalize correlation between geo-locations and texts and propose a novel anchor link prediction framework for matching users across networks. Moreover, our model can alleviate the label scarcity problem by introducing external data. Experimental results on real-world datasets show that our approach outperforms existing methods and achieves state-of-the-art results.