Science audiences, misinformation, and fake news

Polarization and Fake News: Early Warning of Potential Misinformation Targets

Users polarization and confirmation bias play a key role in misinformation spreading on online social media. Our aim is to use this information to determine in advance potential targets for hoaxes and fake news. In this paper, we introduce a general framework for promptly identifying polarizing content on social media and, thus, “predicting” future fake news topics. We validate the performances...

Preventing sensationalistic science and fake news about substance use

An accurately informed public is vital to creating rational substance use, prevention, and treatment policies. Unfortunately, the general public neither has the training nor the time to conduct intensive investigations. Most people get their information via the news media, newsletters, and as sound bites. People often accept what they read as true, especially if it comes from a reasonably reput...

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...