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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Leveraging Commonsense Knowledge on Classifying False News and Determining Checkworthiness of Claims.
TL;DR: In this article, the authors propose to leverage commonsense knowledge for the tasks of false news classification and check-worthy claim detection, and fine-tune the BERT language model with a commonsense question answering task and the aforementioned tasks in a multi-task learning environment.
Posted Content
Fake News Detection through Graph Comment Advanced Learning
TL;DR: Zhang et al. as discussed by the authors proposed a graph comment-user advanced learning framework (GCAL) for detecting fake news on social media, which models user-comment context through network representation learning based on heterogeneous graph neural network.
Fake News Detection using Deep Learning and Machine Learning Methods - A comparative study on short and long texts
TL;DR: Two Datasets, one containing short text statements and the other containing long text articles, are examined, which provide a multi-class architecture in order to assess, compare and prove that long text content is better suited for detecting Fake News than the short text one.
Proceedings ArticleDOI
Review of the Application of Machine Learning in Rumor Detection
TL;DR: Wang et al. as mentioned in this paper presented a survey of rumor detection models from four perspectives: (1) the datasets used in training and verifying the models, (2) the features to detect the rumors, (3) the algorithms of rumor detecting, (4) the metrics used to evaluate the results of the models.
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