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
An anatomical comparison of fake-news and trusted-news sharing pattern on Twitter
TL;DR: An anatomical comparison of fake-news and trusted-news sharing pattern on Twitter is presented, using datasets generated by three different Tweets collection strategies to identify the characteristics of tweets sharing fake- news, and allows to find the users who are more inclined to share misinformation.
Posted Content
Time-Aware Evidence Ranking for Fact-Checking.
TL;DR: This study investigates the hypothesis that the timestamp of an evidence page is crucial to how it should be ranked for a given claim and reveals that time-aware evidence ranking not only surpasses relevance assumptions based purely on semantic similarity or position in a search results list, but also improves veracity predictions of time-sensitive claims in particular.
Proceedings ArticleDOI
Adversary-Aware Rumor Detection
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Vulnerable to Misinformation? Verifi!.
Alireza Karduni,Isaac Cho,Ryan Wesslen,Sashank Santhanam,Svitlana Volkova,Dustin Arendt,Samira Shaikh,Wenwen Dou +7 more
TL;DR: Interviews with experts in digital media, communications, education, and psychology who study misinformation highlight the complexity of the problem of combating misinformation and show promising potential for Verifi2 as an educational tool on misinformation.
Proceedings ArticleDOI
Modeling Behavioral Aspects of Social Media Discourse for Moral Classification
Kristen Johnson,Dan Goldwasser +1 more
TL;DR: Probabilistic Soft Logic is used to build relational models to capture the similarities in language and behavior that obfuscate political messages on Twitter, which reveal the moral foundations underlying the discourse of U.S. politicians online.
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