mlsanityData to 7 Oct 2026

"This is Fake! Shared it by Mistake": Assessing the Intent of Fake News Spreaders

arXiv 2202.04752 ↗First posted 9 Feb 2022cs.SI cs.AIcited by 9

Not on the map. Why?

Abstract (arXiv)

Individuals can be misled by fake news and spread it unintentionally without knowing it is false. This phenomenon has been frequently observed but has not been investigated. Our aim in this work is to assess the intent of fake news spreaders. To distinguish between intentional versus unintentional spreading, we study the psychological explanations of unintentional spreading. With this foundation, we then propose an influence graph, using which we assess the intent of fake news spreaders. Our extensive experiments show that the assessed intent can help significantly differentiate between intentional and unintentional fake news spreaders. Furthermore, the estimated intent can significantly improve the current techniques that detect fake news. To our best knowledge, this is the first work to model individuals' intent in fake news spreading.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 17 of its 46 references that are in the map. First posted on arXiv 9 Feb 2022.

Builds on

17 of 46 references.

References · 17

  1. 2017Attention Is All You Needcited by 59,932
  2. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  3. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
  4. 2020Longformer: The Long-Document Transformercited by 3,362
  5. 2014Learning Fine-grained Image Similarity with Deep Rankingcited by 189
  6. 2017Automatic Detection of Fake Newscited by 145
  7. 2017The spread of low-credibility content by social botscited by 73
  8. 2020ReCOVery: A Multimodal Repository for COVID-19 News Credibility Researchcited by 44
  9. 2020MM-COVID: A Multilingual and Multimodal Data Repository for Combating COVID-19 Disinformationcited by 36
  10. 2020Detection of Novel Social Bots by Ensembles of Specialized Classifierscited by 32
  11. 2020SentiBERT: A Transferable Transformer-Based Architecture for Compositional Sentiment Semanticscited by 30
  12. 2020Causal Understanding of Fake News Dissemination on Social Mediacited by 27
5 more
  1. 2018The Rise of Guardians: Fact-checking URL Recommendation to Combat Fake Newscited by 24
  2. 2019A Topic-Agnostic Approach for Identifying Fake News Pagescited by 17
  3. 2021VoterFraud2020: a Multi-modal Dataset of Election Fraud Claims on Twittercited by 7
  4. 2014Viral Misinformation: The Role of Homophily and Polarizationcited by 4
  5. 2019SciLens: Evaluating the Quality of Scientific News Articles Using Social Media and Scientific Literature Indicatorscited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 4; 2024: 3; 2025: 0; 2026: 1.

9 papers.

  1. 2023Reinforcement Learning-based Counter-Misinformation Response Generation: A Case Study of COVID-19 Vaccine Misinformationcited by 39
  2. 2024Why Misinformation is Created? Detecting them by Integrating Intent Featurescited by 11
  3. 2022Nothing Stands Alone: Relational Fake News Detection with Hypergraph Neural Networkscited by 6
  4. 2023The Challenges of Studying Misinformation on Video-Sharing Platforms During Crises and Mass-Convergence Eventscited by 3
  5. 2023Linguistic-style-aware Neural Networks for Fake News Detectioncited by 2
  6. 2023SeGA: Preference-Aware Self-Contrastive Learning with Prompts for Anomalous User Detection on Twittercited by 2
  7. 2024Semantic Evolvement Enhanced Graph Autoencoder for Rumor Detectioncited by 1
  8. 2024Out-of-distribution Rumor Detection via Test-Time Adaptationcited by 0
  9. 2026Predicting, Evaluating, and Explaining Top Misinformation Spreaders via Archetypal User Behaviorcited by 0