mlsanityData to 14 Oct 2026

How does Truth Evolve into Fake News? An Empirical Study of Fake News Evolution

arXiv 2103.05944 ↗First posted 10 Mar 2021cs.CLcited by 5

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Abstract (arXiv)

Automatically identifying fake news from the Internet is a challenging problem in deception detection tasks. Online news is modified constantly during its propagation, e.g., malicious users distort the original truth and make up fake news. However, the continuous evolution process would generate unprecedented fake news and cheat the original model. We present the Fake News Evolution (FNE) dataset: a new dataset tracking the fake news evolution process. Our dataset is composed of 950 paired data, each of which consists of articles representing the three significant phases of the evolution process, which are the truth, the fake news, and the evolved fake news. We observe the features during the evolution and they are the disinformation techniques, text similarity, top 10 keywords, classification accuracy, parts of speech, and sentiment properties.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 11 of its 38 references that are in the map. First posted on arXiv 10 Mar 2021.

Builds on

11 of 38 references.

References · 11

  1. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,375
  2. 2019Defending Against Neural Fake Newscited by 559
  3. 2017"Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detectioncited by 363
  4. 2017Fake News Detection on Social Media: A Data Mining Perspectivecited by 336
  5. 2018FakeNewsNet: A Data Repository with News Content, Social Context and Spatialtemporal Information for Studying Fake News on Social Mediacited by 285
  6. 2019MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claimscited by 169
  7. 2019Fine-Grained Analysis of Propaganda in News Articlescited by 133
  8. 2016Hoaxy: A Platform for Tracking Online Misinformationcited by 17
  9. 2019Mining Dual Emotion for Fake News Detectioncited by 8
  10. 2019Rumor Detection and Classification for Twitter Datacited by 4
  11. 2014Information Evolution in Social Networkscited by 3

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Cited by year

Citing papers per year: 2024: 3; 2025: 2.

5 papers.

  1. 2024From Skepticism to Acceptance: Simulating the Attitude Dynamics Toward Fake Newscited by 51
  2. 2025The Truth Becomes Clearer Through Debate! Multi-Agent Systems with Large Language Models Unmask Fake Newscited by 29
  3. 2024The Stepwise Deception: Simulating the Evolution from True News to Fake News with LLM Agentscited by 24
  4. 2025A Symbolic Adversarial Learning Framework for Evolving Fake News Generation and Detectioncited by 3
  5. 2024A (More) Realistic Evaluation Setup for Generalisation of Community Models on Malicious Content Detectioncited by 1