# Leveraging the Crowd to Detect and Reduce the Spread of Fake News and Misinformation

arXiv: https://arxiv.org/abs/1711.09918 · HTML: https://mlsanity.com/paper/1711.09918/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2017.

## Built on by (most cited first, 12 of 12)

- [Neural Temporal Point Processes: A Review](https://mlsanity.com/paper/2104.03528/) (2021)
- [Reinforcement Learning-based Counter-Misinformation Response Generation: A Case Study of COVID-19 Vaccine Misinformation](https://mlsanity.com/paper/2303.06433/) (2023)
- [Adapting Fake News Detection to the Era of Large Language Models](https://mlsanity.com/paper/2311.04917/) (2023)
- [Studying Fake News Spreading, Polarisation Dynamics, and Manipulation by Bots: a Tale of Networks and Language](https://mlsanity.com/paper/2109.07909/) (2021)
- [Applying Automatic Text Summarization for Fake News Detection](https://mlsanity.com/paper/2204.01841/) (2022)
- [Fine-Grained Appropriate Reliance: Human-AI Collaboration with a Multi-Step Transparent Decision Workflow for Complex Task Decomposition](https://mlsanity.com/paper/2501.10909/) (2025)
- [Like Article, Like Audience: Enforcing Multimodal Correlations for Disinformation Detection](https://mlsanity.com/paper/2108.13892/) (2021)
- [A comparative analysis of Graph Neural Networks and commonly used machine learning algorithms on fake news detection](https://mlsanity.com/paper/2203.14132/) (2022)
- [Early Detection of Misinformation for Infodemic Management: A Domain Adaptation Approach](https://mlsanity.com/paper/2406.10238/) (2024)
- [Bridging Cognition and Emotion: Empathy-Driven Multimodal Misinformation Detection](https://mlsanity.com/paper/2504.17332/) (2025)
- [Emotionally Aware Moderation: The Potential of Emotion Monitoring in Shaping Healthier Social Media Conversations](https://mlsanity.com/paper/2507.21089/) (2025)
- [Let the Bullets Fly: Multimodal Fake News Detection with Temporal-Aligned Generative Danmaku](https://mlsanity.com/paper/2608.22832/) (2026)

## Abstract (arXiv)

Online social networking sites are experimenting with the following crowd-powered procedure to reduce the spread of fake news and misinformation: whenever a user is exposed to a story through her feed, she can flag the story as misinformation and, if the story receives enough flags, it is sent to a trusted third party for fact checking. If this party identifies the story as misinformation, it is marked as disputed. However, given the uncertain number of exposures, the high cost of fact checking, and the trade-off between flags and exposures, the above mentioned procedure requires careful reasoning and smart algorithms which, to the best of our knowledge, do not exist to date. In this paper, we first introduce a flexible representation of the above procedure using the framework of marked temporal point processes. Then, we develop a scalable online algorithm, Curb, to select which stories to send for fact checking and when to do so to efficiently reduce the spread of misinformation with provable guarantees. In doing so, we need to solve a novel stochastic optimal control problem for stochastic differential equations with jumps, which is of independent interest. Experiments on two real-world datasets gathered from Twitter and Weibo show that our algorithm may be able to effectively reduce the spread of fake news and misinformation.