• Computer Science > Social and Information Networks [Submitted on 28 Jan 2026] Title:Toward Effective Multi-Domain Rumor Detection in Social Networks Using Domain-Gated Mixture-of-Experts View PDFAbstract: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

Article Summaries:

  • Computer Science > Social and Information Networks [Submitted on 28 Jan 2026] Title:Toward Effective Multi-Domain Rumor Detection in Social Networks Using Domain-Gated Mixture-of-Experts View PDFAbstract: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 mitigatin

Sources: