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
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Posted Content
Towards Understanding the Information Ecosystem Through the Lens of Multiple Web Communities
TL;DR: The analysis reveal that fringe Web communities like 4chan's /pol/ and The_Donald subreddit have a disproportionate influence on mainstream communities like Twitter with regard to the dissemination of news and memes, while for Web archiving services, they can be misused to penalize ad revenue from news sources with conflicting ideology.
Book ChapterDOI
Fake News Detection Through Topic Modeling and Optimized Deep Learning with Multi-Domain Knowledge Sources
TL;DR: In this article, a two-step automatic fake news detection model was proposed using bidirectional encoder representations from the Transformers (BERT) model with optimal Neurons and Domain knowledge.
Posted Content
Political Bias and Factualness in News Sharing across more than 100,000 Online Communities
TL;DR: The authors conducted the largest study of news sharing on reddit to date, analyzing more than 550 million links spanning 4 years and found that extremely biased and low factual content is very concentrated, with 99% of such content being shared in only 0.5% of communities, giving credence to the recent strategy of communitywide bans and quarantines.
Book ChapterDOI
Detecting Fake News with Machine Learning
Nagender Aneja,Sandhya Aneja +1 more
TL;DR: This article used Part of Speech and Sentiment Analysis features to detect fake news and found that the top ten features instead of all 43 features gave the accuracy of 0.85 and F-score of 087.
Dissertation
Detection of automatically generated texts
TL;DR: This thesis first introduces different methods of generating free texts that resemble a certain topic and how those texts can be used and sheds light on multiple important research questions about the possibility of detecting automatically generated texts in different setting.
References
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