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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Deep Level Analysis of Legitimacy in Bengali News Sentences
DasSoma,RaiPooja,ChatterjiSanjay +2 more
TL;DR: In this paper, the detection of misinformation in news data has become a very important problem and the potential threat for the adverse effects on society has been identified, which has led to a tremendous increase in the growth in the number of false news articles.
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Classification of Tweets about Reported Events using Neural Networks.
TL;DR: A method to input and concatenate character and word sequences in Japanese Tweets by using convolutional neural networks is proposed and results show that a system using the proposed method and architectures can classify Tweets with an F1 score.
Book ChapterDOI
Understanding the Impact of and Analysing Fake News About COVID-19 in SA
Sthembile Mthethwa,Nelisiwe Dlamini,Nenekazi N. P. Mkuzangwe,Avuya Shibambu,Thato Boateng,Motlatsi Mantsi +5 more
TL;DR: In this paper, the authors analyzed fake news about COVID-19 spread during the South African national lockdown on social media platforms and news outlets; together with the measures put in place by the government i.e. social relief funds and food parcels.
Journal ArticleDOI
Detecting fake news by enhanced text representation with multi-EDU-structure awareness
TL;DR: Zhang et al. as discussed by the authors proposed a multi-EDU-structure awareness model to improve text representation for fake news detection, namely EDU4FD, which uses EDU expressing content with an intermediate granularity to detect fake news.
Journal ArticleDOI
Distinguishing between fake news and satire with transformers
TL;DR: The authors applied transformers to the task of separating satirical news from fake news and achieved an improvement of 0.0429 (5.2%) in F1 and 0.0522 (6.4%) in accuracy.
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