Rhetorical Structure Approach for Online Deception Detection: A Survey (2022.lrec-1)
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| Challenge: | Existing studies on how people use language to inform and misinform are relevant. |
| Approach: | They analyze how discourse structure is applied to fake news detection on the web and social media. |
| Outcome: | The proposed framework is applied to fake news and fake reviews detection on the web and social media. |
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| Challenge: | Existing methods for capturing discourse-level structure of fake news articles rely on annotated corpora. |
| Approach: | They propose to incorporate hierarchical discourse-level structure of fake and real news articles into detection methods . they propose to learn and construct a discourse- level structure for fake/real news articles . |
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Exploring the Role of Argument Structure in Online Debate Persuasion (2020.emnlp-main)
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| Challenge: | Existing work in NLP has shown that linguistic features extracted from debate text and features encoding the characteristics of the audience are both critical in persuasion studies. |
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| Challenge: | Existing systems focused on the surface words, ignoring the linguistic structure of the texts. |
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Improving Cross-domain, Cross-lingual and Multi-modal Deception Detection (2022.acl-srw)
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| Challenge: | Deception detection is a deliberate choice to mislead to gain some advantage or avoid some penalty. |
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Automatic Detection of Fake News (C18-1)
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| Challenge: | a growing number of fake news detection tools are needed to identify trustworthy news sources. |
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A Survey on Natural Language Processing for Fake News Detection (2020.lrec-1)
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| Challenge: | Automated fake news detection is a critical but challenging problem in NLP . social media has accelerated the spread of fake news, threatening public safety . |
| Approach: | They describe the challenges involved in fake news detection and describe related tasks . they outline promising research directions and highlight the difference between fake news and related tasks. |
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BREAKING! Presenting Fake News Corpus for Automated Fact Checking (P19-2)
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| Challenge: | a new study shows that fake news spreads faster than mainstream articles on the same topic . however, there is no dataset containing compelling fake and questionable news articles . |
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Disentangling Structure and Style: Political Bias Detection in News by Inducing Document Hierarchy (2023.findings-emnlp)
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| Challenge: | a new method to detect political bias in news articles overcomes this domain dependency . partisan bias exists in various social issues, including the 2016 presidential election . |
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Fact Checking or Psycholinguistics: How to Distinguish Fake and True Claims? (D19-66)
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| Challenge: | Using psycholinguistic features to distinguish lies from true statements is a difficult task and a problem to be solved. |
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Detection and Resolution of Rumors and Misinformation with NLP (2020.coling-tutorials)
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| Challenge: | Detecting false and misleading claims on the web is a sub-field of NLP . this half-day tutorial presents the theory behind each of these steps and the state-of-the-art solutions. |
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