Heterogeneous Subgraph Transformer for Fake News Detection

arXiv:2404.13192v1 Announce Type: new
Abstract: Fake news is pervasive on social media, inflicting substantial harm on public discourse and societal well-being. We investigate the explicit structural information and textual features of news pieces by constructing a heterogeneous graph concerning the relations among news topics, entities, and content. Through our study, we reveal that fake news can be effectively detected in terms of the atypical heterogeneous subgraphs centered on them, which encapsulate the essential semantics and intricate relations between news elements. …

abstract arxiv cs.ai cs.cl detection discourse fake fake news features graph harm information media public relations social social media study textual through topics transformer type