An important challenge in the process of tracking and detecting the dissemination of misinformation is to understand the gap in the political views between people that engage with the so called ”fake news”. A possible factor responsible for this gap is opinion polarization, which may prompt the general public to classify content that they disagree or want to discredit as fake. In this work, we ...

While news media biases and propaganda are a persistent problem for interpreting the true state of world affairs, increasing reliance on the internet as a primary news source has enabled the formation of hyper-partisan echo chambers and an industry where outlets benefit from purveying “fake news”. The presence of intentionally adversarial news sources challenges linguistic modeling of news arti...

Fake news pose serious threat to our society nowadays, particularly due to its wide spread through social networks. While human fact checkers cannot handle such tremendous information online in real time, AI technology can be leveraged to automate fake news detection. The first step leading to a sophisticated fake news detection system is the stance detection between statement and body text. In...

We propose the first multistage intervention framework that tackles fake news in social networks by combining reinforcement learning with a point process network activity model. The spread of fake news and mitigation events within the network is modeled by a multivariate Hawkes process with additional exogenous control terms. By choosing a feature representation of states, defining mitigation a...

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...

Identifying the veracity of a news article is an interesting problem while automating this process can be a challenging task. Detection of a news article as fake is still an open question as it is contingent on many factors which the current state-of-the-art models fail to incorporate. In this paper, we explore a subtask to fake news identification, and that is stance detection. Given a news ar...

This paper reports on a writing style analysis of hyperpartisan (i.e., extremely onesided) news in connection to fake news. It presents a large corpus of 1,627 articles that were manually fact-checked by professional journalists from BuzzFeed. The articles originated from 9 well-known political publishers, 3 each from the mainstream, the hyperpartisan left-wing, and the hyperpartisan right-wing...