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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Journal ArticleDOI
Different Faces of False: The Spread and Curtailment of False Information in the Black Panther Twitter Discussion
TL;DR: A study of the most tweeted about movie ever (Black Panther) in which the spread of false information of four different types is compared to the ad hoc Twitter community response and helps illustrate the importance of investigating “on-the-ground” community responses to fake news and other types of digital false information.
Journal ArticleDOI
Deception detection on social media: A source-based perspective
TL;DR: In this paper , a source-based method in a machine learning framework was proposed to detect fake news, rumors, conspiracies, hoaxes, and other forms of deception in online social networks.
Proceedings ArticleDOI
Bias Mitigation for Evidence-aware Fake News Detection by Causal Intervention
TL;DR: Inspired by the success of causal inference, a novel framework for debiasing evidence-based fake news detection is proposed, which makes conventional predictions and counterfactual predictions simultaneously in the testing stage, where counterfactUAL predictions are based on the intervened evidence.
Journal ArticleDOI
Morality Classification in Natural Language Text
TL;DR: The authors presented a number of shallow and deep learning models of moral stance and moral foundations classification, and introduced a novel corpus of texts labelled with moral foundation scores, and a novel approach to fine-grained, human-centric moral foundation classification that is, to the best of our knowledge, among the first NLP studies of this kind.
Proceedings ArticleDOI
A Semantic Model for Context-Based Fake News Detection on Social Media
TL;DR: In this article, a taxonomy for entities classification was developed to describe classes extracted from the taxonomy towards fully semantically describing concepts, relations, instances, and axioms, which would enhance fake news detection through semantic annotation for contextual features of news objects and datasets.
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