Knowing who to trust and what to believe in the presence of conflicting information

The Information Age has created an increasing abundance of data and has, thanks to the rise of the Internet, made that knowledge instantly available to humans and computers alike. This is not without caveats, however, as though we may read a document, ask an expert, or locate a fact nearly effortlessly, we lack a ready means to determine whether we should actually believe them. We seek to address this problem with a computational trust system capable of substituting for the user's informed, subjective judgement, with the understanding that truth is not objective and instead depends upon one's prior knowledge and beliefs, a philosophical point with deep practical implications. First, however, we must consider the even more basic question of how the trustworthiness of an information source can be expressed: measuring the trustworthiness of a person, document, or publisher as the mere percentage of true claims it makes can be extraordinarily misleading at worst, and uninformative at best. Instead of providing simple accuracy, we instead provide a comprehensive set of trust metrics, calculating the source's truthfulness, completeness, and bias, providing the user with our trust judgement in a way that is both understandable and actionable. We then consider the trust algorithm itself, starting with the baseline of determining the truth by taking a simple vote that assumes all information sources are equally trustworthy, and quickly move on to fact-finders , iterative algorithms capable of estimating the trustworthiness of the source in addition to the believability of the claims, and proceed to incorporate increasing amounts of information and declarative prior knowledge into the fact-finder's trust decision via the Generalized and Constrained Fact-Finding frameworks while still maintaining the relative simplicity and tractability of standard fact-finders.