Svitlana Volkova|Kyle Shaffer|Jin Yea Jang|Nathan O. Hodas
Separating Facts from Fiction: Linguistic Models to Classify Suspicious and Trusted News Posts on Twitter
Pacific Northwest National Laboratory|Battelle|Battelle|Pacific Northwest National Laboratory|Battelle|Pacific Northwest National Laboratory|Pacific Northwest National Laboratory|Battelle
pew research polls report 62 percent us adults get news social media gottfried shearer 2016in december poll 64 percent us adults said madeup news caused great deal confusion facts current events barthel et al 2016fabricated stories social media ranging deliberate propaganda hoaxes satire contributes confusion addition serious effects global stabilityin work build predictive models classify 130 thousand news posts suspicious verified predict four subtypes suspicious news satire hoaxes clickbait propagandawe show neural network models trained tweet content social network interactions outperform lexical modelsunlike previous work deception detection find adding syntax grammar features models improve performanceincorporating linguistic features improves classification results however social interaction features informative finergrained separation four types suspicious news posts