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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TL;DR: In this article , the authors used the Recurrent Neural Network (RNN) to detect the fake news and achieved a good accuracy compared to existing natural language processing methods and achieved good results.
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TL;DR: This study presents a study of moral categories classification from text based on Moral Foundations Theory using machine learning supervised methods and proposes the development of models based on contextual-sensitive embeddings methods for IMFC and PMFC.
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Leveraging Community and Author Context to Explain the Performance and Bias of Text-Based Deception Detection Models
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Knowledge Enhanced Multi-modal Fake News Detection.
TL;DR: Wang et al. as mentioned in this paper transform the problem of detecting fake news into a subgraph classification task, where entities and relations are extracted from each news item to form a single knowledge graph, where a news item is represented by a sub-graph.
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