Cross-Platform Modeling of Users' Behavior on Social Media

TL;DR

This paper presents a cross-platform user behavior modeling approach that combines data from NetEase Music and Sina Weibo to analyze correlations between music preferences, personality traits, and social characteristics, enabling more precise user profiling.

Contribution

The study introduces a novel method for integrating structured and unstructured social media data across platforms to build detailed user portraits and analyze behavior correlations.

Findings

Music preferences are linked to geographic and social factors.

Dog lovers tend to prefer sad music more than cat lovers.

The approach can be adapted to other verticals for automatic user profiling.

Abstract

With the booming development and popularity of mobile applications, different verticals accumulate abundant data of user information and social behavior, which are spontaneous, genuine and diversified. However, each platform describes user's portraits in only certain aspect, resulting in difficult combination of those internet footprints together. In our research, we proposed a modeling approach to analyze user's online behavior across different social media platforms. Structured and unstructured data of same users shared by NetEase Music and Sina Weibo have been collected for cross-platform analysis of correlations between music preference and other users' characteristics. Based on music tags of genre and mood, genre cluster of five groups and mood cluster of four groups have been formed by computing their collected song lists with K-means method. Moreover, with the help of user data of Weibo, correlations between music preference (i.e. genre, mood) and Big Five personalities (BFPs) and basic information (e.g. gender, resident region, tags) have been comprehensively studied, building up full-scale user portraits with finer grain. Our findings indicate that people's music preference could be linked with their real social activities. For instance, people living in mountainous areas generally prefer folk music, while those in urban areas like pop music more. Interestingly, dog lovers could love sad music more than cat lovers. Moreover, our proposed cross-platform modeling approach could be adapted to other verticals, providing an online automatic way for profiling users in a more precise and comprehensive way.