Toward Effective Multi-Domain Rumor Detection in Social Networks Using Domain-Gated Mixture-of-Experts

TL;DR

This paper introduces PerFact, a large multi-domain rumor dataset and a novel domain-gated Mixture-of-Experts model that effectively detects rumors across diverse social media domains with high accuracy.

Contribution

The study presents a new multi-domain rumor dataset and a domain-gated Mixture-of-Experts model that improves rumor detection performance across different domains.

Findings

Achieved an F1-score of 79.86% on multi-domain rumor detection.

Demonstrated state-of-the-art accuracy of 79.98% in classifying rumors.

Validated the effectiveness of domain gating in handling domain shifts.

Abstract

Social media platforms have become key channels for spreading and tracking rumors due to their widespread accessibility and ease of information sharing. Rumors can continuously emerge across diverse domains and topics, often with the intent to mislead society for personal or commercial gain. Therefore, developing methods that can accurately detect rumors at early stages is crucial to mitigating their negative impact. While existing approaches often specialize in single-domain detection, their performance degrades when applied to new domains due to shifts in data distribution, such as lexical patterns and propagation dynamics. To bridge this gap, this study introduces PerFact, a large-scale multi-domain rumor dataset comprising 8,034 annotated posts from the X platform, annotated into two primary categories: rumor (including true, false, and unverified rumors) and non-rumor. Annotator agreement, measured via Fleiss' Kappa ($\kappa = 0.74$), ensures high-quality labels. This research further proposes an effective multi-domain rumor detection model that employs a domain gate to dynamically aggregate multiple feature representations extracted through a Mixture-of-Experts method. Each expert combines CNN and BiLSTM networks to capture local syntactic features and long-range contextual dependencies. By leveraging both textual content and publisher information, the proposed model classifies posts into rumor and non-rumor categories with high accuracy. Evaluations demonstrate state-of-the-art performance, achieving an F1-score of 79.86\% and an accuracy of 79.98\% in multi-domain settings. Keywords: Rumor Detection, Multi-Domain, Natural Language Processing, Social Networks, Mixture-of-Experts Model