A Multi-View Approach Based on Naming Behavioral Modeling for Aligning Chinese User Accounts across Multiple Networks

TL;DR

This paper introduces a multi-view framework for aligning Chinese user accounts across social networks by modeling naming behaviors, demonstrating improved accuracy over existing methods through experiments on Sina Weibo and Twitter.

Contribution

It proposes the MCUA framework that integrates multiple models for Chinese account name matching, addressing a gap in existing cross-network user alignment methods.

Findings

MCUA outperforms existing methods in Chinese account alignment

Identifies the most valuable features for different name matching types

Determines optimal learning models for the alignment task

Abstract

Hundreds of millions of Chinese people have become social network users in recent years, and aligning the accounts of common Chinese users across multiple social networks is valuable to many inter-network applications, e.g., cross-network recommendation, cross-network link prediction. Many methods have explored the proper ways of utilizing account name information into aligning the common English users' accounts. However, how to properly utilize the account name information when aligning the Chinese user accounts remains to be detailedly studied. In this paper, we firstly discuss the available naming behavioral models as well as the related features for different types of Chinese account name matchings. Secondly, we propose the framework of Multi-View Cross-Network User Alignment (MCUA) method, which uses a multi-view framework to creatively integrate different models to deal with different types of Chinese account name matchings, and can consider all of the studied features when aligning the Chinese user accounts. Finally, we conduct experiments to prove that MCUA can outperform many existing methods on aligning Chinese user accounts between Sina Weibo and Twitter. Besides, we also study the best learning models and the top-k valuable features of different types of name matchings for MCUA over our experimental data sets.