Teaching Machines to Detect Fake News Is Really Hard

One of the most difficult parts of limiting the spread of fake news is that humans have a hard time filtering what is legit from what is bogus. But if humans can't tell the difference between what's real and fake, could machines do any better? In an attempt to answer this question, UC Santa Barbara computer scientist William Wang created LIAR, the largest ever database of fake news in an effort to train machines to automatically detect deception. Comprised of 12,836 examples of statements culled from a decade's worth of short statements from the Pulitzer prize-winning politifact.com, LIAR is an order of magnitude larger than any other fake news database that has been created in response to the 2016 election. To help a machine learning algorithm understand the statements, each of the 12,836 quotes was tagged with information about the truthfulness, subject, context, speaker, state, party, and prior history of inaccurate statements, in addition to a "lengthy analysis report" for the statement.