An algorithm-based system that identifies telltale linguistic cues in fake news stories could provide news aggregator and social media sites like Google News with a new weapon in the fight against misinformation. The University of Michigan researchers who developed the system have demonstrated that it's comparable to and sometimes better than humans at correctly identifying fake news stories. Catching fake stories before they have real consequences can be difficult, as aggregator and social media sites today rely heavily on human editors who often can't keep up with the influx of news. The challenge to building a fake news detector lies not in building the algorithm itself, but in finding the right data with which to train that algorithm. Satirical news, for example, is easy to collect, but its use of irony and absurdity make it less useful for training an algorithm to detect fake news that's meant to mislead. Ultimately, Mihalcea's team created its own data, crowdsourcing an online team that reverse-engineered verified genuine news stories into fakes. Study participants, recruited with the help of Amazon Mechanical Turk, were paid to turn short, actual news stories into similar but fake news items, mimicking the journalistic style of the articles. The details of the new system and the dataset that the team used to build it are freely available, and Mihalcea says they could be used by news sites or other entities to build their own fake news detection systems. Read more