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Hierarchical Propagation Networks for Fake News Detection: Investigation and Exploitation

arXiv 1903.09196 ↗First posted 21 Mar 2019cs.SIcited by 32

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Abstract (arXiv)

Consuming news from social media is becoming increasingly popular. However, social media also enables the widespread of fake news. Because of its detrimental effects brought by social media, fake news detection has attracted increasing attention. However, the performance of detecting fake news only from news content is generally limited as fake news pieces are written to mimic true news. In the real world, news pieces spread through propagation networks on social media. The news propagation networks usually involve multi-levels. In this paper, we study the challenging problem of investigating and exploiting news hierarchical propagation network on social media for fake news detection. In an attempt to understand the correlations between news propagation networks and fake news, first, we build a hierarchical propagation network from macro-level and micro-level of fake news and true news; second, we perform a comparative analysis of the propagation network features of linguistic, structural and temporal perspectives between fake and real news, which demonstrates the potential of utilizing these features to detect fake news; third, we show the effectiveness of these propagation network features for fake news detection. We further validate the effectiveness of these features from feature important analysis. Altogether, this work presents a data-driven view of hierarchical propagation network and fake news and paves the way towards a healthier online news ecosystem.

Key facts. Cited by 32 papers in the map (data through 14 Oct 2026). First posted on arXiv 21 Mar 2019.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 11; 2022: 6; 2023: 4; 2024: 3; 2025: 5; 2026: 3.

32 papers.

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  1. 2021User Preference-aware Fake News Detectioncited by 79
  2. 2021Embracing Domain Differences in Fake News: Cross-domain Fake News Detection using Multi-modal Datacited by 50
  3. 2021A Survey on Multimodal Disinformation Detectioncited by 47
  4. 2021Integrating Pattern- and Fact-based Fake News Detection via Model Preference Learningcited by 25
  5. 2021Hierarchical Multi-head Attentive Network for Evidence-aware Fake News Detectioncited by 17
  6. 2022Multi-modal Misinformation Detection: Approaches, Challenges and Opportunitiescited by 15
  7. 2023MetaAdapt: Domain Adaptive Few-Shot Misinformation Detection via Meta Learningcited by 8
  8. 2021Studying Fake News Spreading, Polarisation Dynamics, and Manipulation by Bots: a Tale of Networks and Languagecited by 6
  9. 2024Multimodal Clickbait Detection by De-confounding Biases Using Causal Representation Inferencecited by 6
  10. 2023Graph Mining for Cybersecurity: A Surveycited by 5
  11. 2025Collaborative Evolution: Multi-Round Learning Between Large and Small Language Models for Emergent Fake News Detectioncited by 5
  12. 2022Applying Automatic Text Summarization for Fake News Detectioncited by 4
20 more
  1. 2022Integrating Human-in-the-loop into Swarm Learning for Decentralized Fake News Detectioncited by 3
  2. 2023Unsupervised Domain-agnostic Fake News Detection using Multi-modal Weak Signalscited by 3
  3. 2021Towards Trustworthy Deception Detection: Benchmarking Model Robustness across Domains, Modalities, and Languagescited by 2
  4. 2021Like Article, Like Audience: Enforcing Multimodal Correlations for Disinformation Detectioncited by 2
  5. 2022A Weibo Dataset for the 2022 Russo-Ukrainian Crisiscited by 2
  6. 2025Misinformation Detection using Large Language Models with Explainabilitycited by 2
  7. 2026RW-Post: Auditable Evidence-Grounded Multimodal Fact-Checking in the Wildcited by 2
  8. 2023Leveraging Social Interactions to Detect Misinformation on Social Mediacited by 1
  9. 2021REMOD: Relation Extraction for Modeling Online Discoursecited by 0
  10. 2021Human-in-the-Loop Disinformation Detection: Stance, Sentiment, or Something Else?cited by 0
  11. 2021A Review of Web Infodemic Analysis and Detection Trends across Multi-modalities using Deep Neural Networkscited by 0
  12. 2022A comparative analysis of Graph Neural Networks and commonly used machine learning algorithms on fake news detectioncited by 0
  13. 2022Hope Speech Detection on Social Media Platformscited by 0
  14. 2024MAPX: An explainable model-agnostic framework for the detection of false information on social media networkscited by 0
  15. 2024Enriching GNNs with Text Contextual Representations for Detecting Disinformation Campaigns on Social Mediacited by 0
  16. 2025Exploring Content and Social Connections of Fake News with Explainable Text and Graph Learningcited by 0
  17. 2025Explainable Fraud Detection with GNNExplainer and Shapley Valuescited by 0
  18. 2025RW-Post: Auditable Evidence-Grounded Multimodal Fact-Checking in the Wildcited by 0
  19. 2026Spectral Analysis of Fake News Propagationcited by 0
  20. 2026Fake News Theories: Harnessing Disciplinary Insights for Computational Modeling, Detection, and Explanationcited by 0