7 accepted papers
The proliferation of fake news videos on social media has heightened the demand for credible verification systems. While existing methods focus on detecting false content, generating human-readable explanations for such predictions remains a critical challenge. Current approaches suffer from spuriou
Document-level event factuality identification (DEFI) assesses the veracity degree to which an event mentioned in a document has happened, which is crucial for many natural language processing tasks. Previous work assesses event factuality by solely relying on the semantic information within a singl…
Multi-modal Rumor Detection (MRD) has emerged as a crucial research hotpot due to the continuous rise in the spread of multi-modal information on the Internet. Existing studies frequently employ traditional single-classifier models, which cannot accurately classify challenging positive samples. More…
With the rapid development of social media, rumor detection on social media has become vitally crucial. Multi-modal fusion and representation play an important role in Multi-modal Rumor Detection (MRD). However, few works learn multi-modal invariant feature and discover the multi-modal class distrib…
Document-level Event Factuality Identification (DEFI) refers to identifying the degree of certainty that a specific event occurs in a document. Previous studies on DEFI failed to link the document-level event factuality with various sentence-level factuality values in the same document. In this pape…
Document-level Event Factuality Identification (DEFI) predicts the factuality of a specific event based on a document from which the event can be derived, which is a fundamental and crucial task in Natural Language Processing (NLP). However, most previous studies only considered sentence-level task…
Document-level Event Factuality Identification (DEFI) predicts the event factuality according to the current document, and mainly depends on event-related tokens and sentences. However, previous studies relied on annotated information and did not filter irrelevant and noisy texts. Therefore, this pa…