Rumors, smears, and conspiracy theories can now spread quickly through email, blogs, and other social media. Recipients of such messages may not question their validity. Moreover, even upon careful investigation and reflection, not everyone will agree about the validity of particular claims. This project will develop tools that help people make personal assessments of credibility. Rather than relying on particular sources as authoritative arbiters of ground truth, the goal is to minimize the amount of "social implausibility." That is, the tool will identify assertions that are disbelieved by "similar" people (those who, after careful consideration, someone tended to agree with in the past) or come from sources that someone has tended to disagree with. A text mining system for online media will be developed to extract controversial assertions and the beliefs expressed by users about those assertions. Comparisons of beliefs about common assertions, and retractions or updates to beliefs, will be tracked as part of personalized reputation measures.
This work is the first attempt to formally address the automatic assessment of information credibility based on text mining and social computational systems. The techniques will provide the solution to many challenging research problems in information retrieval and reputation networks. The techniques are broadly applicable to other domains where the credibility of content and reputation of sources is a concern, to help a broad class of information consumers. Prototype tools will be released freely and demonstrated in high schools, thereby building awareness of the diversity of beliefs around topics of public interest.
