The emergence of "Fake News" and misinformation via online news and social media has spurred an interest in computational tools to combat this phenomenon. In this paper we present a new "Related Fact Checks" service, which can help a reader critically evaluate an article and make a judgment on its veracity by bringing up fact checks that are relevant to the article. We describe the core technical problems that need to be solved in building a "Related Fact Checks" service, and present results from an evaluation of an implementation.
Voters are often highly dependent on partisanship to structure their preferences toward political candidates and policy proposals. What conditions enable partisan cues to “dominate” public opinion? Here I theorize that variation in voters’ reliance on partisanship results, in part, from the opportunities their environment provides to learn about politics. A conjoint experiment and an observational study of voting in congressional elections both support the expectation that more detailed information environments reduce the role of partisanship in candidate choice. These findings clarify previously unexplained cross-study variation in party cue effects. They also challenge competing claims that partisan cues inhibit responsiveness to such a degree that voters fail to use other information or that high-information environments increase voter reliance on partisanship.  Although many studies suggest that voters learn about political facts with prejudice toward their preexisting beliefs, none have fully characterized all inputs to Bayes’ Rule, leaving uncertainty about the magnitude of bias. This paper evaluates political learning by first highlighting the importance of careful measures of each input and then presenting a statistical model and experiment that measure the magnitude of departure from Bayesian learning. Subjects learn as cautious Bayesians, updating their beliefs at about 73 of perfect application of Bayes’ Rule. They are also modestly biased. For information consistent with prior beliefs, subject learning is not statistically distinguishable from perfect Bayesian. Inconsistent information, however, corresponds to learning less than perfect. Despite bias, beliefs do not polarize. With small monetary incentives for accuracy, aggregate beliefs converge toward common truth. Cautious Bayesian learning appears to be a reasonable model of how citizens process pol... Even when checked by fact checkers, facts are often still open to preexisting bias and doubt.