An Emotion-Enriched and Psycholinguistics Features-Based Approach for Rumor Detection on Online Social Media

Abstract

Social media platforms and online communication tools, once lauded as sources of information, unity, and global connectivity, are now breeding grounds for the unprecedented spread of false, misleading, and manipulative information. Rumor is one type of such information. It consists of untrue information and deceptive messages and is constructed to manipulate emotions. Consequently, emotions are crucial in determining the veracity of rumors. In this paper, we introduce an emotion-enhanced and psycholinguistic features-based approach for rumors detection on social social media. It entails detecting rumors utilizing lexicons and various linguistic features-based learning approaches, primarily by extracting the psychological association of words with their emotions. Emotional and psycholinguistic features are extracted from both posts and comments to enhance the approach and make rumor detection more effective. Using word-level GloVe embedding, the semantic relationships between a post and its comments and their underlying emotions are preserved. The proposed method is evaluated on the popular PHEME dataset and compared to various baselines and SOTA methods, demonstrating substantially superior performance for rumor detection on social media platforms.