Our work considers leveraging crowd signals for detecting fake news and is motivated by tools recently introduced by Facebook that enable users to flag fake news. By aggregating users’ flags, our goal is to select a small subset of news every day, send them to an expert (e.g., via a third-party factchecking organization), and stop the spread of news identified as fake by an expert. The main obj...

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...

We investigated the differential diffusion of all of the verified true and false news stories distributed on Twitter from 2006 to 2017. The data comprise ~126,000 stories tweeted by ~3 million people more than 4.5 million times. We classified news as true or false using information from six independent fact-checking organizations that exhibited 95 to 98% agreement on the classifications. Falseh...

In this study, we looked at the effect of promotion in the speed and width of spread of information on the Internet by tracking the diffusion of news articles over a social network. Speed of spread means the number of readers that the news has reached in a given time, while width of spread means how far the story has travelled from the news originator within the social network. After analyzing ...

The press in performing its functions can not be separated from all acts of fraud and irregularities committed by the subjects both public, (journalists, media, council, etc.), even government. Criminal law has two main elements namely, first is existence a norm, which prohibition or order (rule). Second, sanctions for violations norm form threats with criminal law. This research aims: 1) to an...