Fake or for real? A fake news workshop

Real Fake Real Fake

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...

Is It Fake or Real?

In this paper, we describe a new methodology to separate photographs and computer generated images . We introduce the Progressive Randomization (PR) technique that captures the statistical properties of each one of these classes. Using only statistical descriptors of the least significant bit (LSB) occurrences, our method already performs as well or better than some comparable existing techniqu...

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"