Network Information Enhances Unreliable News Domain Detection
2026-08-03 • Social and Information Networks
Social and Information NetworksMachine Learning
AI summaryⓘ
The authors studied how to tell reliable news sources from unreliable ones by looking at how websites are shared together on Telegram chats, rather than just analyzing individual articles. They found that reliable and unreliable news sites tend to cluster together in these sharing networks. By using special neural networks that understand these network patterns (Graph Neural Networks), they got better at predicting which domains are trustworthy compared to methods that ignore the network structure. Their approach works even when the content of the news can't be analyzed, showing that network connections alone provide useful clues about reliability.
Content-based detectionNews reliabilityDomain co-sharing networkAssortative mixingGraph Neural NetworksGraphSAGETelegram chatsContent embeddingsSpreading dynamicsNetwork topology
Authors
Raphaela Keßler, Roman David Ventzke, Viola Priesemann, Giordano De Marzo
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.