G. L. Ciampaglia1, P. Shiralkar1, Luis M. Rocha1,2 J. Bollen1, F. Menczer1, and A. Flamminni1
1School of Informatics, Indiana University, Bloomington IN, USA
2FLAD Computational Biology Collaboratorium, Instituto Gulbenkian de Ciencia, Portugal
Citation: G.L. Ciampaglia, P. Shiralkar, L.M. Rocha, J. Bollen, F. Menczer, A. Flammini [2015]. "Computational fact checking from knowledge networks." PLoS One. 10(6): e0128193. doi:10.1371/journal.pone.0128193.
The full text and pdf re-print are available from the PLoS ONE site. Due to mathematical notation and graphics, only the abstract is presented here. The arXiv:1501.03471 is also available.
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.
Keywords: Luis M. Rocha, complex networks, knowledge networks, distance closure, metric closure, machine learning, fact-checking