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
A Theory-based Deep-Learning Approach to Detecting Disinformation in Financial Social Media
TL;DR: In this article , a theory-based, novel deep-learning approach (called TRNN) is proposed to detect disinformation in financial social media, which uses deep learning and data-centric augmentation.
Posted Content
What Makes Online Communities 'Better'? Measuring Values, Consensus, and Conflict across Thousands of Subreddits
TL;DR: This article found that there is 47.4% more disagreement over how safe communities are than disagreement over other aspects of communities' current state, that longstanding communities place 30.1% more emphasis on trustworthiness than newer communities, and that recently joined redditors perceive their communities more positively than more senior redditors.
Proceedings ArticleDOI
Hybrid Approach and Architecture to Detect Fake News on Twitter in Real-Time using Neural Networks
Madusha Prasanjith Thilakarathna,Vihanga Ashinsana Wijayasekara,Yasiru Gamage,Kavindi Hanshani Peiris,Chanuka Abeysinghe,Intizar Rafaideen,Prathieshna Vekneswaran +6 more
TL;DR: In this paper, the authors discuss the implementation of a browser extension which will identify fake news on Twitter using deep learning models with a focus on real-world applicability, architectural stability and scalability of such a solution.
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Understanding the dynamics emerging from infodemics: A call to action for interdisciplinary research.
TL;DR: It is argued that, to get a deep understanding of the dynamics emerging from infodemics, the fields of Business and Economics should integrate the perspectives of Computer Science and Information Systems, (Computational) Linguistics, and Cognitive Science into the wider context of economic systems and propose a way to do so.
Journal ArticleDOI
Spatio-temporal approach for classification of COVID-19 pandemic fake news
TL;DR: In this paper , the impact of spatial and temporal information features for classification of fake news was explored, which to the best of our knowledge has not been explored yet, and these features are directly not available in any news article available online.
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