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

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. * This work was done during Jooyeon Kim's internship at the Max Planck Institute for Software Systems. 1 https://www.washingtonpost.com/posteverything/wp/2016/06/16/why-the-post-truth-political-era-might-be-around-for-a-while/ 2 https://www.theguardian.com/commentisfree/2016/may/13/boris-johnson-donald-trump-post-truth-politician 3 https://newsroom.fb.com/news/2016/12/news-feed-fyi-addressing-hoaxes-and-fake-news/ 4 https://www.washingtonpost.com/news/the-switch/wp/2017/06/29/twitter-is-looking-for-ways-to-let-users-flag-fake-news/ 5 their feeds, they have a choice to flag the story as misinformation and, if the story receives enough flags, it is directed to a coalition of independent organizations 6 , signatories of Poynter's International Fact Checking Code of Principles 7 , for fact checking. If the fact checking organizations identify a story as misinformation, it gets flagged as disputed and may also appear lower in the users' feeds, reducing the number of people who are exposed to misinformation. In this context, online social networking sites are giving advice to its millions of users on how to spot misinformation online 8 . However, the above mentioned procedure requires careful reasoning and smart algorithms which, to the best of our knowledge, are nonexistent to date:-Uncertain number of exposures: the spread of information over social networking sites is a stochastic process, which may depend on, e.g., the information content, the users' influence and the network structure. Thus, the number of users exposed to different stories varies greatly and we need to consider probabilistic exposure models to capture this uncertainty.-Fact checking is costly: given the myriad of (fake) sto...

The facts of fake news: A research review

“…These findings should be able to guide efforts to address the problem of fake news. For example, studies in computer science and engineering have proposed ways to detect fake news using automation (e.g., Dey, Rafi, Parash, Arko, & Chakrabarty, 2018;Kim, Tabibian, Oh, Schlkopf, & Rodriguez, 2018;Tschiatschek, Singla, Rodriguez, Merchant, & Krause, 2018). In mass communication studies, a widely researched area when it comes to addressing fake news is that of fact checking.…”

False News On Social Media

“…Finally, Section 5 presents a few works which tackle the problem of mitigation against false news on social media, following recent announcements from major platforms to favor crowd-sourcing initiatives against malicious information [27].…”

“…Main social networking platforms, from Facebook to Twitter, have recently provided to their users tools to combat disinformation [27], an approach which seems reasonable enough to tackle the problem of disinformation without raising censorship alerts. Resorting to the wisdom of the crowd, as discussed above, can be effective at identifying malicious news items and prevent from misinformation spreading on social networks.…”

The Rise of Guardians

“…In natural disasters and emergency situations, misinformation was investigated as well [15,19,58]. Several works attempted to detect rumors as soon as possible using disputed signals [32,58], leveraging network embedding [54] and employing collective data sources [20,40]. However, there is no work about combating fake news once it has been debunked.…”