Detecting Antagonistic and Allied Communities on Social Media

TL;DR

This paper introduces a framework for detecting and understanding the relationships between communities on social media by analyzing user attitudes and social interactions, revealing alliances and antagonisms.

Contribution

The paper proposes a novel framework that jointly models user attitudes and social interactions to detect community relations, addressing a gap in existing community detection methods.

Findings

Framework effectively detects community relations in real-world datasets.

User attitudes reflect inter-community alliances and antagonisms.

Experimental results validate the framework's efficacy.

Abstract

Community detection on social media has attracted considerable attention for many years. However, existing methods do not reveal the relations between communities. Communities can form alliances or engage in antagonisms due to various factors, e.g., shared or conflicting goals and values. Uncovering such relations can provide better insights to understand communities and the structure of social media. According to social science findings, the attitudes that members from different communities express towards each other are largely shaped by their community membership. Hence, we hypothesize that inter-community attitudes expressed among users in social media have the potential to reflect their inter-community relations. Therefore, we first validate this hypothesis in the context of social media. Then, inspired by the hypothesis, we develop a framework to detect communities and their relations by jointly modeling users' attitudes and social interactions. We present experimental results using three real-world social media datasets to demonstrate the efficacy of our framework.