"Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection
William Yang Wang
- Vol. 2, pp 422-426
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TLDR
Li et al. as discussed by the authors designed a hybrid convolutional neural network to integrate meta-data with text and showed that this hybrid approach can improve a text-only deep learning model.Abstract:
Automatic fake news detection is a challenging problem in deception detection, and it has tremendous real-world political and social impacts. However, statistical approaches to combating fake news has been dramatically limited by the lack of labeled benchmark datasets. In this paper, we present LIAR: a new, publicly available dataset for fake news detection. We collected a decade-long, 12.8K manually labeled short statements in various contexts from PolitiFact.com, which provides detailed analysis report and links to source documents for each case. This dataset can be used for fact-checking research as well. Notably, this new dataset is an order of magnitude larger than previously largest public fake news datasets of similar type. Empirically, we investigate automatic fake news detection based on surface-level linguistic patterns. We have designed a novel, hybrid convolutional neural network to integrate meta-data with text. We show that this hybrid approach can improve a text-only deep learning model.read more
Citations
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Book ChapterDOI
The Role of Personality and Linguistic Patterns in Discriminating Between Fake News Spreaders and Fact Checkers
TL;DR: The CheckerOrSpreader model, a model that can classify a user as a potential fact checker or a potential fake news spreader, is proposed and shows that leveraging linguistic patterns and personality traits can improve the performance in differentiating between checkers and spreaders.
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r/Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection
TL;DR: This work presents Fakeddit, a novel multimodal dataset consisting of over 1 million samples from multiple categories of fake news, and constructs hybrid text+image models and performs extensive experiments for multiple variations of classification, demonstrating the importance of the novel aspect of multimodality and fine-grained classification unique to Fakeddam.
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The Limitations of Stylometry for Detecting Machine-Generated Fake News.
TL;DR: This paper showed that stylometry is limited against machine-generated misinformation, and highlighted the need for non-stylometry approaches in detecting machine generated misinformation and open up the discussion on the desired evaluation benchmarks.
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News recommender system: a review of recent progress, challenges, and opportunities
Shaina Raza,Chen Ding +1 more
TL;DR: In this paper, a survey of the state-of-the-art news recommender systems (NRS) is presented, which highlights the major challenges faced by the NRS and identifies the possible solutions from the state of the art.
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Unreliable Users Detection in Social Media: Deep Learning Techniques for Automatic Detection
TL;DR: A deep investigation of the features that both from an automatic and a human point of view, are more predictive for the identification of social network profiles accountable for spreading fake news in the online environment shows which information best enables machines and humans to detect malicious users.
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