Fake news: the case for a purely consumer-oriented explication

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...

The Economics of "Fake News"

Exploiting Tri-Relationship for Fake News Detection

Social media for news consumption is becoming popular nowadays. The low cost, easy access and rapid information dissemination of social media bring benefits for people to seek out news timely. However, it also causes the widespread of fake news, i.e., low-quality news pieces that are intentionally fabricated. The fake news brings about several negative effects on individual consumers, news ecos...

Neural Stance Detectors for Fake News Challenge

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...

News Feature: The genuine problem of fake news.

In 2010 computer scientist Filippo Menczer heard a conference talk about some phony news reports that had gone viral during a special Senate election in Massachusetts. “I was struck,” saysMenczer. He and his team at IndianaUniversity Bloomington had been tracking early forms of spam since 2005, looking mainly at then-new social bookmarking sites such as https://del. icio.us/. “We called it soci...