Multi-agent fact checking

We formulate the problem of fake news detection using multi-agent 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, which reflects the true validity of the statement with some probability 1−π_i. In other words, agent i misclassifies each statement with error probability π_i∈(0,1), where the parameter π_i models the (un)trustworthiness of agent i. We present an algorithm to learn the unreliability parameters, resulting in a multi-agent fact-checking algorithm. Furthermore, we extensively analyze the discrete-time limit of our algorithm.