Title: 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.