Separating Facts from Fiction: Linguistic Models to Classify Suspicious and Trusted News Posts on Twitter
Svitlana Volkova,Kyle Shaffer,Jin Yea Jang,Nathan O. Hodas +3 more
- Vol. 2, pp 647-653
TLDR
This work builds 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, and shows that neural network models trained on tweet content and social network interactions outperform lexical models.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.read more
Citations
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Combating fake news by empowering fact-checked news spread via topology-based interventions
TL;DR: In this article, a novel information diffusion and intervention technique to combat the spread of false news is proposed, which mainly relies on defining the potential super-spreaders in a social network based on their centrality metrics.
Proceedings ArticleDOI
Misinfo Reaction Frames: Reasoning about Readers’ Reactions to News Headlines
TL;DR: Gabriel, Skyler, Hallinan, Maarten Sap, Pemi Nguyen, Franziska Roesner, Eunsol Choi, Yejin Choi, and Eun Choi as discussed by the authors presented a paper at the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).
Book ChapterDOI
Misinformation and Disinformation on Social Media: An Updated Survey of Challenges and Current Trends
TL;DR: Fake news detection is intrinsically hard since we have to cope with textual data; moreover the early detection requirement, to prevent wide diffusion, makes things even harder as mentioned in this paper , which is why researchers have been keeping proposing new solutions to deal with new nuances of the problem.
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Misinformation Detection on YouTube Using Video Captions.
TL;DR: In this paper, the authors proposed an approach that uses state-of-the-art NLP techniques to extract features from video captions (subtitles) to evaluate their approach, utilizing a publicly accessible and labeled dataset for classifying videos as misinformation or not.
Journal ArticleDOI
Lay it Out: Detecting of Fake News Publishers through Website Structure Data
Lia Bozarth,Ceren Budak +1 more
TL;DR: The authors proposed a website structure based domain-level fake news detection model that has performance results surprisingly comparable to that of existing content-based methods, highlighting that fake news sites have more clustered subpages and more ads, whereas traditional news sites are more substantive and more likely to contain staff links.
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