Fake News Analysis Modeling Using Quote Retweet

Quote Clustering in Online News

The notion that information moves through social networks has been widely discussed[3], however, with the growing availability of large digital corpora, the ability to quantitatively model this phenomenon is new. To this end we explore a large corpus of online news quotations looking for cases of noisy reproduction and the factors which influence such noise. An essential step in this process is...

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...

Fake News Detection using Stacked Ensemble of Classifiers

Fake news has become a hotly debated topic in journalism. In this paper, we present our entry to the 2017 Fake News Challenge which models the detection of fake news as a stance classification task that finished in 11th place on the leader board. Our entry is an ensemble system of classifiers developed by students in the context of their coursework. We show how we used the stacking ensemble met...