mlsanityData to 7 Oct 2026

Nothing Stands Alone: Relational Fake News Detection with Hypergraph Neural Networks

arXiv 2212.12621 ↗First posted 24 Dec 2022cs.SI cs.LGcited by 6

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Abstract (arXiv)

Nowadays, fake news easily propagates through online social networks and becomes a grand threat to individuals and society. Assessing the authenticity of news is challenging due to its elaborately fabricated contents, making it difficult to obtain large-scale annotations for fake news data. Due to such data scarcity issues, detecting fake news tends to fail and overfit in the supervised setting. Recently, graph neural networks (GNNs) have been adopted to leverage the richer relational information among both labeled and unlabeled instances. Despite their promising results, they are inherently focused on pairwise relations between news, which can limit the expressive power for capturing fake news that spreads in a group-level. For example, detecting fake news can be more effective when we better understand relations between news pieces shared among susceptible users. To address those issues, we propose to leverage a hypergraph to represent group-wise interaction among news, while focusing on important news relations with its dual-level attention mechanism. Experiments based on two benchmark datasets show that our approach yields remarkable performance and maintains the high performance even with a small subset of labeled news data.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 31 of its 53 references that are in the map. First posted on arXiv 24 Dec 2022.

Builds on

31 of 53 references.

References · 31

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19 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 2; 2025: 1; 2026: 1.

6 papers.

  1. 2023Can LLM-Generated Misinformation Be Detected?cited by 173
  2. 2023HyperFormer: Learning Expressive Sparse Feature Representations via Hypergraph Transformercited by 8
  3. 2024Catching Chameleons: Detecting Evolving Disinformation Generated using Large Language Modelscited by 5
  4. 2024Sequential Classification of Misinformationcited by 0
  5. 2025The Impact of Data Characteristics on GNN Evaluation for Detecting Fake Newscited by 0
  6. 2026Large Language Models for Causal Relations Extraction in Social Media: A Validation Framework for Disaster Intelligencecited by 0