Separating Facts from Fiction: Linguistic Models to Classify Suspicious and Trusted News Posts on Twitter
Pew research polls report 62 percent of U.S. adults get news on social media (Gottfried and Shearer, 2016). In a December poll, 64 percent of U.S. adults said that “made-up news” has caused a “great deal of confusion” about the facts of current events (Barthel et al., 2016). Fabricated stories spread in social media, ranging from deliberate propaganda to hoaxes and satire, contributes to this confusion in addition to having serious effects on global stability. In this work we build predictive models to classify 130 thousand news tweets as suspicious or verified, and predict four subtypes of suspicious news – satire, hoaxes, clickbait and propaganda. We demonstrate that neural network models trained on tweet content and social network interactions outperform lexical models. Unlike previous work on deception detection, we find that adding syntax and grammar features to our models decreases performance. Incorporating linguistic features, including bias and subjectivity, improves classification results, however social interaction features are most informative for finer-grained separation between our four types of suspicious news posts.
- Research Organization:
- Pacific Northwest National Laboratory (PNNL), Richland, WA (US)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC05-76RL01830
- OSTI ID:
- 1373869
- Report Number(s):
- PNNL-SA-123856; 453040300
- Country of Publication:
- United States
- Language:
- English
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