Toward computational fact-checking

Published

Journal Article

Our news are saturated with claims of "facts" 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, e.g., is a claim "cherry-picking"? This paper proposes a framework that models claims based on structured data as parameterized queries. A key insight is that we can learn a lot about a claim by perturbing its parameters and seeing how its conclusion changes. This framework lets us formulate practical fact-checking tasks-reverse-engineering (often intentionally) vague claims, and countering questionable claims-as computational problems. Along with the modeling framework, we develop an algorithmic framework that enables efficient instantiations of "meta" algorithms by supplying appropriate algorithmic building blocks. We present real-world examples and experiments that demonstrate the power of our model, efficiency of our algorithms,and usefulness of their results. © 2014 VLDB Endowment.

Full Text

Duke Authors

Cited Authors

  • Wu, Y; Agarwal, PK; Li, C; Yang, J; Yu, C

Published Date

  • January 1, 2014

Published In

Volume / Issue

  • 7 / 7

Start / End Page

  • 589 - 600

Electronic International Standard Serial Number (EISSN)

  • 2150-8097

Digital Object Identifier (DOI)

  • 10.14778/2732286.2732295

Citation Source

  • Scopus