Distributed Fact Checking: Learning Unreliability

We formulate the problem of fake news detection using distributed fact-checkers (agents) with unknown reliability. The stream of news/statements is modeled as an independent and identically distributed binary source (to represent true and false statements). Upon observing a news, agent i labels the news as true or false that reflects the true validity of the statement with some probability 1-P. In other words, agent i misclassified each statement with error probability Pin(0,1), where the parameter P models the (un)trustworthiness of agent i. We present an algorithm to learn the unreliability parameters, resulting in a distributed fact checking algorithm. For the two-agent case, we extensively analyze the discrete-time limit of our algorithm.