Birds of a Feather Rumor Together? Exploring Homogeneity and Conversation Structure in Social Media for Rumor Detection

Abstract

Rumors in social media represent a severe problem prevailing in today's society. Previous studies on automated rumor detection have shown that the topological information specific to social media is a vital clue for debunking rumors. However, existing automatic rumor detection approaches either oversimplify the graph structure or ignore this crucial clue. To address this issue, we propose a model that explores homogeneity and conversation structure to identify rumors. Our model learns more comprehensive and precise representations by modeling follower-following relationships of users, simulating the propagation layout of tweets, and connecting responders' behavior. The experimental results on two public Twitter datasets show that our model's performance outperforms other state-of-the-art baseline models. Furthermore, the experimental results prove our hypothesis that birds of a feather rumor together. The results demonstrate that both the conversation structure and the friend network's homogeneity are significant for checking the veracity of a suspicious tweet.

Keywords

• Rumor detection
• homogeneity
• conversation structure
• graph neural network
• deep learning