Network Information Enhances Unreliable News Domain Detection

• URL: http://arxiv.org/abs/2608.02399v1
• Date: Mon, 03 Aug 2026 15:43:14 GMT
• Title: Network Information Enhances Unreliable News Domain Detection
• Abstract summary: We ask whether network structure can improve news reliability classification.<n>From URL-sharing patterns in Telegram chats, we build a statistically validated domain co-sharing network.<n>We find assortative mixing by reliability: low-reliability domains group together, as do reliable ones.
• Score: 0.38887448816036313
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• CAT: Can Trust be Predicted with Context-Awareness in Dynamic Heterogeneous Networks? [10.476593803368358]
  Graph Neural Networks (GNNs) have emerged as a promising approach for trust prediction.<n>These models fail to capture trust dynamicity, leading to questionable inferences.<n>We propose CAT, the first Context-Aware GNN-based Trust prediction model.
  arXiv  Detail & Related papers  (2025-12-12T08:00:28Z)
• On Balancing Sparsity with Reliable Connectivity in Distributed Network Design with Random K-out Graphs [8.957579200590985]
  Sparsity ensures communication overhead remains low, while reliable connectivity is tied to reliable communication and inference on decentralized data reservoirs and computational resources.<n>A class of network models called random K-out graphs appear widely as a to balance connectivity and sparsity, especially in settings with limited trust.<n>We present theorems to inform the choice of network parameters that guarantee reliable connectivity in regimes where nodes can be finite or unreliable.
  arXiv  Detail & Related papers  (2025-08-16T01:29:16Z)
• BotTrans: A Multi-Source Graph Domain Adaptation Approach for Social Bot Detection [55.31623652907614]
  We propose a multi-source graph domain adaptation model named textitBotTrans for detecting social bots.<n>We first leverage the labeling knowledge shared across multiple source networks to establish a cross-source-domain topology.<n>We then aggregate cross-domain neighbor information to enhance the discriminability of source node embeddings.
  arXiv  Detail & Related papers  (2025-06-12T02:10:36Z)
• MetaGMT: Improving Actionable Interpretability of Graph Multilinear Networks via Meta-Learning Filtration [6.102559098873098]
  We present MetaGMT, a meta-learning framework that enhances explanation fidelity through a novel bi-level optimization approach.<n>We demonstrate that MetaGMT significantly improves both explanation quality (AUC-ROC, Precision@K) and robustness to spurious patterns.<n>Our work contributes to building more trustworthy and actionable GNN systems for real-world applications.
  arXiv  Detail & Related papers  (2025-05-26T03:07:58Z)
• Detection and Discovery of Misinformation Sources using Attributed Webgraphs [3.659498819753633]
  We introduce a novel attributed webgraph dataset with labeled news domains and their connections to outlinking and backlinking domains. We demonstrate the success of graph neural networks in detecting news site reliability using these attributed webgraphs. We also introduce and evaluate a novel graph-based algorithm for discovering previously unknown misinformation news sources.
  arXiv  Detail & Related papers  (2024-01-04T17:47:36Z)
• KGTrust: Evaluating Trustworthiness of SIoT via Knowledge Enhanced Graph Neural Networks [63.531790269009704]
  Social Internet of Things (SIoT) is a promising and emerging paradigm that injects the notion of social networking into smart objects (i.e., things) Due to the risks and uncertainty, a crucial and urgent problem to be settled is establishing reliable relationships within SIoT, that is, trust evaluation. We propose a novel knowledge-enhanced graph neural network (KGTrust) for better trust evaluation in SIoT.
  arXiv  Detail & Related papers  (2023-02-22T14:24:45Z)
• Reliable Federated Disentangling Network for Non-IID Domain Feature [62.73267904147804]
  In this paper, we propose a novel reliable federated disentangling network, termed RFedDis. To the best of our knowledge, our proposed RFedDis is the first work to develop an FL approach based on evidential uncertainty combined with feature disentangling. Our proposed RFedDis provides outstanding performance with a high degree of reliability as compared to other state-of-the-art FL approaches.
  arXiv  Detail & Related papers  (2023-01-30T11:46:34Z)
• Generalizability of Adversarial Robustness Under Distribution Shifts [57.767152566761304]
  We take a first step towards investigating the interplay between empirical and certified adversarial robustness on one hand and domain generalization on another. We train robust models on multiple domains and evaluate their accuracy and robustness on an unseen domain. We extend our study to cover a real-world medical application, in which adversarial augmentation significantly boosts the generalization of robustness with minimal effect on clean data accuracy.
  arXiv  Detail & Related papers  (2022-09-29T18:25:48Z)
• Can pruning improve certified robustness of neural networks? [106.03070538582222]
  We show that neural network pruning can improve empirical robustness of deep neural networks (NNs) Our experiments show that by appropriately pruning an NN, its certified accuracy can be boosted up to 8.2% under standard training. We additionally observe the existence of certified lottery tickets that can match both standard and certified robust accuracies of the original dense models.
  arXiv  Detail & Related papers  (2022-06-15T05:48:51Z)
• TrustGNN: Graph Neural Network based Trust Evaluation via Learnable Propagative and Composable Nature [63.78619502896071]
  Trust evaluation is critical for many applications such as cyber security, social communication and recommender systems. We propose a new GNN based trust evaluation method named TrustGNN, which integrates smartly the propagative and composable nature of trust graphs. Specifically, TrustGNN designs specific propagative patterns for different propagative processes of trust, and distinguishes the contribution of different propagative processes to create new trust.
  arXiv  Detail & Related papers  (2022-05-25T13:57:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.