More than “Fake News”?

Fake News in Social Networks

We model the spread of news as a social learning game on a network. Agents can either endorse or oppose a claim made in a piece of news, which itself may be either true or false. Agents base their decision on a private signal and their neighbors’ past actions. Given these inputs, agents follow strategies derived via multi-agent deep reinforcement learning and receive utility from acting in acco...

The Economics of "Fake News"

Automatic Detection of Fake News

The proliferation of misleading information in everyday access media outlets such as social media feeds, news blogs, and online newspapers have made it challenging to identify trustworthy news sources, thus increasing the need for computational tools able to provide insights into the reliability of online content. In this paper, we focus on the automatic identification of fake content in online...

This Just In: Fake News Packs a Lot in Title, Uses Simpler, Repetitive Content in Text Body, More Similar to Satire than Real News

The problem of fake news has gained a lot of attention as it is claimed to have had a significant impact on 2016 US Presidential Elections. Fake news is not a new problem and its spread in social networks is well-studied. Often an underlying assumption in fake news discussion is that it is written to look like real news, fooling the reader who does not check for reliability of the sources or th...

Fake News, Real Consequences: Recruiting Neural Networks for the Fight Against Fake News

The Fake News Challenge (FNC-1) is a public competition that aims to find automatic methods for detecting fake news. The dataset for the challenge consists of headline-body pairs, with the objective being to classify the pairs as unrelated, agreeing, disagreeing, or discussing. We developed four neural network models for FNC-1, two using a feed-forward architecture and two using a recurrent arc...