Separating Facts from Fiction: Linguistic Models to Classify Suspicious and Trusted News Posts on Twitter

Svitlana Volkova, Kyle Shaffer, Jin Yea Jang, Nathan Hodas


Abstract
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 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 posts as suspicious or verified, and predict four sub-types of suspicious news – satire, hoaxes, clickbait and propaganda. We show 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 does not improve performance. Incorporating linguistic features improves classification results, however, social interaction features are most informative for finer-grained separation between four types of suspicious news posts.
Anthology ID:
P17-2102
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Editors:
Regina Barzilay, Min-Yen Kan
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
647–653
Language:
URL:
https://aclanthology.org/P17-2102
DOI:
10.18653/v1/P17-2102
Bibkey:
Cite (ACL):
Svitlana Volkova, Kyle Shaffer, Jin Yea Jang, and Nathan Hodas. 2017. Separating Facts from Fiction: Linguistic Models to Classify Suspicious and Trusted News Posts on Twitter. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 647–653, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
Separating Facts from Fiction: Linguistic Models to Classify Suspicious and Trusted News Posts on Twitter (Volkova et al., ACL 2017)
Copy Citation:
PDF:
https://aclanthology.org/P17-2102.pdf
Dataset:
 P17-2102.Datasets.zip