News verification is a process of determining whether a particular news report is truthful or deceptive. Deliberately deceptive (fabricated) news creates false conclusions in the readers’ minds. Truthful (authentic) news matches the writer’s knowledge. How do you tell the difference between the two in an automated way? To investigate this question, we analyzed rhetorical structures, discourse constituent parts and their coherence relations in deceptive and truthful news sample from NPR’s “Bluff the Listener”. Subsequently, we applied a vector space model to cluster the news by discourse feature similarity, achieving 63% accuracy. Our predictive model is not significantly better than chance (56% accuracy), though comparable to average human lie detection abilities (54%). Methodological limitations and future improvements are discussed. The long-term goal is to uncover systematic language differences and inform the core methodology of the news verification system.
Citation of this paper:
Victoria L. Rubin, Niall J. Conroy and Yimin Chen. "Towards News Verification: Deception Detection Methods for News Discourse" HICSS2015 (2015)
Computational Linguistics Commons, Journalism Studies Commons, Library and Information Science Commons, Mass Communication Commons, Semantics and Pragmatics Commons