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

TL;DR

This paper introduces a novel, scalable online algorithm called Curb that uses a marked temporal point process framework to strategically select social media stories for fact checking, effectively reducing misinformation spread.

Contribution

It presents a new stochastic optimal control approach for misinformation mitigation, with a scalable algorithm and provable guarantees, addressing a gap in existing methods.

Findings

Effective reduction of misinformation spread demonstrated on Twitter and Weibo datasets.

Curb algorithm outperforms baseline strategies in reducing fake news dissemination.

The approach provides a new framework for real-time, crowd-powered misinformation control.

Abstract

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.