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

arXiv: https://arxiv.org/abs/2103.05944 · HTML: https://mlsanity.com/paper/2103.05944/

## 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 (most cited first, 11 of 11)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Defending Against Neural Fake News](https://mlsanity.com/paper/1905.12616/) (2019)
- ["Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection](https://mlsanity.com/paper/1705.00648/) (2017)
- [Fake News Detection on Social Media: A Data Mining Perspective](https://mlsanity.com/paper/1708.01967/) (2017)
- [FakeNewsNet: A Data Repository with News Content, Social Context and Spatialtemporal Information for Studying Fake News on Social Media](https://mlsanity.com/paper/1809.01286/) (2018)
- [MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims](https://mlsanity.com/paper/1909.03242/) (2019)
- [Fine-Grained Analysis of Propaganda in News Articles](https://mlsanity.com/paper/1910.02517/) (2019)
- [Hoaxy: A Platform for Tracking Online Misinformation](https://mlsanity.com/paper/1603.01511/) (2016)
- [Mining Dual Emotion for Fake News Detection](https://mlsanity.com/paper/1903.01728/) (2019)
- [Rumor Detection and Classification for Twitter Data](https://mlsanity.com/paper/1912.08926/) (2019)
- [Information Evolution in Social Networks](https://mlsanity.com/paper/1402.6792/) (2014)

## Built on by (most cited first, 5 of 5)

- [From Skepticism to Acceptance: Simulating the Attitude Dynamics Toward Fake News](https://mlsanity.com/paper/2403.09498/) (2024)
- [The Truth Becomes Clearer Through Debate! Multi-Agent Systems with Large Language Models Unmask Fake News](https://mlsanity.com/paper/2505.08532/) (2025)
- [The Stepwise Deception: Simulating the Evolution from True News to Fake News with LLM Agents](https://mlsanity.com/paper/2410.19064/) (2024)
- [A Symbolic Adversarial Learning Framework for Evolving Fake News Generation and Detection](https://mlsanity.com/paper/2508.19633/) (2025)
- [A (More) Realistic Evaluation Setup for Generalisation of Community Models on Malicious Content Detection](https://mlsanity.com/paper/2404.01822/) (2024)

## 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.