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arXiv · 2608.02399

Network Information Enhances Unreliable News Domain Detection

Raphaela Keßler·Roman David Ventzke·Viola Priesemann·Giordano De Marzo
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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.

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Raphaela Keßler, Roman David Ventzke, Viola Priesemann, Giordano De Marzo. 2026-08-03. Network Information Enhances Unreliable News Domain Detection. https://arxiv.org/abs/2608.02399

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