# The science of fake news

arXiv: https://arxiv.org/abs/2307.07903 · HTML: https://mlsanity.com/paper/2307.07903/

## Key facts

- Cited by 97 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jul 2023.

## Built on by (most cited first, 30 of 97)

- [Can LLM-Generated Misinformation Be Detected?](https://mlsanity.com/paper/2309.13788/) (2023)
- [Towards LLM-based Fact Verification on News Claims with a Hierarchical Step-by-Step Prompting Method](https://mlsanity.com/paper/2310.00305/) (2023)
- [Disinformation Detection: An Evolving Challenge in the Age of LLMs](https://mlsanity.com/paper/2309.15847/) (2023)
- [EX-FEVER: A Dataset for Multi-hop Explainable Fact Verification](https://mlsanity.com/paper/2310.09754/) (2023)
- [The Stepwise Deception: Simulating the Evolution from True News to Fake News with LLM Agents](https://mlsanity.com/paper/2410.19064/) (2024)
- [Fact-checking information from large language models can decrease headline discernment](https://mlsanity.com/paper/2308.10800/) (2023)
- [The simulation of judgment in LLMs](https://mlsanity.com/paper/2502.04426/) (2025)
- [Support or Refute: Analyzing the Stance of Evidence to Detect Out-of-Context Mis- and Disinformation](https://mlsanity.com/paper/2311.01766/) (2023)
- [How malicious AI swarms can threaten democracy: The fusion of agentic AI and LLMs marks a new frontier in information warfare](https://mlsanity.com/paper/2506.06299/) (2025)
- [A Guide to Misinformation Detection Data and Evaluation](https://mlsanity.com/paper/2411.05060/) (2024)
- [Meaning Is Not A Metric: Using LLMs to make cultural context legible at scale](https://mlsanity.com/paper/2505.23785/) (2025)
- [The Moltbook Illusion: Separating Human Influence from Emergent Behavior in AI Agent Societies](https://mlsanity.com/paper/2602.07432/) (2026)
- [Countering Misinformation via Emotional Response Generation](https://mlsanity.com/paper/2311.10587/) (2023)
- [Fake Artificial Intelligence Generated Contents (FAIGC): A Survey of Theories, Detection Methods, and Opportunities](https://mlsanity.com/paper/2405.00711/) (2024)
- [Promotional Language and the Adoption of Innovative Ideas in Science](https://mlsanity.com/paper/2406.02798/) (2024)
- [Harmful YouTube Video Detection: A Taxonomy of Online Harm and MLLMs as Alternative Annotators](https://mlsanity.com/paper/2411.05854/) (2024)
- [BiDeV: Bilateral Defusing Verification for Complex Claim Fact-Checking](https://mlsanity.com/paper/2502.16181/) (2025)
- [The Hidden Threat in Plain Text: Attacking RAG Data Loaders](https://mlsanity.com/paper/2507.05093/) (2025)
- [Reliability Estimation of News Media Sources: Birds of a Feather Flock Together](https://mlsanity.com/paper/2404.09565/) (2024)
- [The FIGNEWS Shared Task on News Media Narratives](https://mlsanity.com/paper/2407.18147/) (2024)
- [MegaFake: A Theory-Driven Dataset of Fake News Generated by Large Language Models](https://mlsanity.com/paper/2408.11871/) (2024)
- [Adversarial Style Augmentation via Large Language Model for Robust Fake News Detection](https://mlsanity.com/paper/2406.11260/) (2024)
- [Before It's Too Late: A State Space Model for the Early Prediction of Misinformation and Disinformation Engagement](https://mlsanity.com/paper/2502.04655/) (2025)
- [CMIE: Combining MLLM Insights with External Evidence for Explainable Out-of-Context Misinformation Detection](https://mlsanity.com/paper/2505.23449/) (2025)
- [JailNewsBench: Multi-Lingual and Regional Benchmark for Fake News Generation under Jailbreak Attacks](https://mlsanity.com/paper/2603.01291/) (2026)
- [Stanceformer: Target-Aware Transformer for Stance Detection](https://mlsanity.com/paper/2410.07083/) (2024)
- [Face the Facts! Evaluating RAG-based Pipelines for Professional Fact-Checking](https://mlsanity.com/paper/2412.15189/) (2024)
- [Echoes of Power: Investigating Geopolitical Bias in US and China Large Language Models](https://mlsanity.com/paper/2503.16679/) (2025)
- [A Revisit of Fake News Dataset with Augmented Fact-checking by ChatGPT](https://mlsanity.com/paper/2312.11870/) (2023)
- [Fake News Detection: It's All in the Data!](https://mlsanity.com/paper/2407.02122/) (2024)

## Abstract (arXiv)

Fake news emerged as an apparent global problem during the 2016 U.S. Presidential election. Addressing it requires a multidisciplinary effort to define the nature and extent of the problem, detect fake news in real time, and mitigate its potentially harmful effects. This will require a better understanding of how the Internet spreads content, how people process news, and how the two interact. We review the state of knowledge in these areas and discuss two broad potential mitigation strategies: better enabling individuals to identify fake news, and intervention within the platforms to reduce the attention given to fake news. The cooperation of Internet platforms (especially Facebook, Google, and Twitter) with researchers will be critical to understanding the scale of the issue and the effectiveness of possible interventions.