Network Information Enhances Unreliable News Domain Detection

Abstract:Content-based detection of unreliable news is increasingly difficult, as low-reliability sources mimic credible journalism and generative AI makes fabricated content harder to flag. We ask whether network structure can improve news reliability classification, taking a domain-level approach that shifts the focus from individual articles to source reliability. From URL-sharing patterns in Telegram chats, we build a statistically validated domain co-sharing network and find assortative mixing by reliability: low-reliability domains group together, as do reliable ones. Exploiting this structure, we compare Graph Neural Networks against network-unaware baselines using both content-aware features (multilingual text embeddings) and content-agnostic features (spreading dynamics). GNNs consistently outperform Multi-Layer Perceptrons on identical features, with GraphSAGE best in both settings (accuracy 0.63 with content, 0.53 without), a 13-14% relative gain over the network-unaware baseline. Network topology thus systematically improves domain reliability assessment, and remains effective even when content analysis is infeasible.

Social and Information Networks (cs.SI); Machine Learning (cs.LG); Physics and Society (physics.soc-ph)

arXiv:2608.02399 [cs.SI]

(or arXiv:2608.02399v1 [cs.SI] for this version)

https://doi.org/10.48550/arXiv.2608.02399 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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