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Database Syst."],"published-print":{"date-parts":[[2017,3,31]]},"abstract":"<jats:p>Our media is saturated with claims of \u201cfacts\u201d made from data. Database research has in the past focused on how to answer queries, but has not devoted much attention to discerning more subtle qualities of the resulting claims, for example, is a claim \u201ccherry-picking\u201d? This article proposes a framework that models claims based on structured data as parameterized queries. Intuitively, with its choice of the parameter setting, a claim presents a particular (and potentially biased) view of the underlying data. A key insight is that we can learn a lot about a claim by \u201cperturbing\u201d its parameters and seeing how its conclusion changes. For example, a claim is not robust if small perturbations to its parameters can change its conclusions significantly. This framework allows us to formulate practical fact-checking tasks\u2014reverse-engineering vague claims, and countering questionable claims\u2014as computational problems. Along with the modeling framework, we develop an algorithmic framework that enables efficient instantiations of \u201cmeta\u201d algorithms by supplying appropriate algorithmic building blocks. 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