With the vast amount of data available in today's world, particularly on the web, it is common to find conflicting information from different sources. Given an input consisting of conflicting claims from multiple sources of unknown trustworthiness and reliability, truth discovery algorithms aim to evaluate which claims should be believed and which sources should be trusted. The evaluations of trust and belief should cohere with one another, so that a claim receives a high belief ranking if it is backed up by trustworthy sources and vice versa.

This project investigates truth discovery from practical and theoretical perspectives. On the practical side, a number of algorithms from the literature are implemented in software and analysed. On the theoretical side, a formal framework is developed to study truth discovery from a general point of view, allowing results to be proved and comparisons made between truth discovery and related areas in the literature. Desirable properties of truth discovery algorithms are defined in the framework, and we consider whether they are satisfied by a particular real-world algorithm, Sums.