Fig 3. Automatic truth assessments for simple factual statements. In each confusion matrix, rows represent subjects and columns represent objects. The diagonals represent true statements. Higher truth values are mapped to colors of increasing intensity. (a) Films winning the Oscar for Best Movie and their directors, grouped by decade of award (see the complete list in the S1 Text). (b) US presidents and their spouses, denoted by initials.

Computational Fact Checking from Knowledge Networks

Abstract

Traditional fact checking by expert journalists cannot keep up with the enormous volume of information that is now generated online. Computational fact checking may significantly enhance our ability to evaluate the veracity of dubious information. Here we show that the complexities of human fact checking can be approximated quite well by finding the shortest path between concept nodes under properly defined semantic proximity metrics on knowledge graphs. Framed as a network problem this approach is feasible with efficient computational techniques. We evaluate this approach by examining tens of thousands of claims related to history, entertainment, geography, and biographical information using a public knowledge graph extracted from Wikipedia. Statements independently known to be true consistently receive higher support via our method than do false ones. These findings represent a significant step toward scalable computational fact-checking methods that may one day mitigate the spread of harmful misinformation.

Publication
PLOS ONE 10 p. 1-13
Date

BibTeX

@Article{Ciampaglia2015b,
  Title                    = {Computational Fact Checking from Knowledge Networks},
  Author                   = {Ciampaglia, Giovanni Luca AND Shiralkar, Prashant AND Rocha, Luis M. AND Bollen, Johan AND Menczer, Filippo AND Flammini, Alessandro},
  Journal                  = {PLOS ONE},
  Year                     = {2015},

  Month                    = {06},
  Number                   = {6},
  Pages                    = {1-13},
  Volume                   = {10},

  Abstract                 = {Traditional fact checking by expert journalists cannot keep up with the enormous volume of information that is now generated online. Computational fact checking may significantly enhance our ability to evaluate the veracity of dubious information. Here we show that the complexities of human fact checking can be approximated quite well by finding the shortest path between concept nodes under properly defined semantic proximity metrics on knowledge graphs. Framed as a network problem this approach is feasible with efficient computational techniques. We evaluate this approach by examining tens of thousands of claims related to history, entertainment, geography, and biographical information using a public knowledge graph extracted from Wikipedia. Statements independently known to be true consistently receive higher support via our method than do false ones. These findings represent a significant step toward scalable computational fact-checking methods that may one day mitigate the spread of harmful misinformation.},
  Doi                      = {10.1371/journal.pone.0128193},
  Publisher                = {Public Library of Science},
  Url                      = {https://doi.org/10.1371/journal.pone.0128193}
}