Mining Unfollow Behavior in Large-Scale Online Social Networks via Spatial-Temporal Interaction

TL;DR

This paper investigates the complex factors influencing unfollow behavior in large-scale social networks, introduces a new dataset, and proposes a model that effectively captures spatial-temporal interactions to improve unfollow prediction accuracy.

Contribution

It constructs a large-scale Weibo dataset with detailed user attributes and content, and develops a novel UMHI model that captures spatial-temporal interactions for unfollow prediction.

Findings

UMHI outperforms baseline methods by 16.44% in precision

Both spatial and temporal attributes are crucial for unfollow prediction

Interaction effects significantly influence unfollow behavior

Abstract

Online Social Networks (OSNs) evolve through two pervasive behaviors: follow and unfollow, which respectively signify relationship creation and relationship dissolution. Researches on social network evolution mainly focus on the follow behavior, while the unfollow behavior has largely been ignored. Mining unfollow behavior is challenging because user's decision on unfollow is not only affected by the simple combination of user's attributes like informativeness and reciprocity, but also affected by the complex interaction among them. Meanwhile, prior datasets seldom contain sufficient records for inferring such complex interaction. To address these issues, we first construct a large-scale real-world Weibo dataset, which records detailed post content and relationship dynamics of 1.8 million Chinese users. Next, we define user's attributes as two categories: spatial attributes (e.g., social role of user) and temporal attributes (e.g., post content of user). Leveraging the constructed dataset, we systematically study how the interaction effects between user's spatial and temporal attributes contribute to the unfollow behavior. Afterwards, we propose a novel unified model with heterogeneous information (UMHI) for unfollow prediction. Specifically, our UMHI model: 1) captures user's spatial attributes through social network structure; 2) infers user's temporal attributes through user-posted content and unfollow history; and 3) models the interaction between spatial and temporal attributes by the nonlinear MLP layers. Comprehensive evaluations on the constructed dataset demonstrate that the proposed UMHI model outperforms baseline methods by 16.44% on average in terms of precision. In addition, factor analyses verify that both spatial attributes and temporal attributes are essential for mining unfollow behavior.