Missing Counter-Evidence Renders NLP Fact-Checking Unrealistic for Misinformation

• URL: http://arxiv.org/abs/2210.13865v1
• Date: Tue, 25 Oct 2022 09:40:48 GMT
• Title: Missing Counter-Evidence Renders NLP Fact-Checking Unrealistic for Misinformation
• Authors: Max Glockner, Yufang Hou, Iryna Gurevych
• Abstract summary: Misinformation emerges in times of uncertainty when credible information is limited. This is challenging for NLP-based fact-checking as it relies on counter-evidence, which may not yet be available.
• Score: 67.69725605939315
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Misinformation emerges in times of uncertainty when credible information is limited. This is challenging for NLP-based fact-checking as it relies on counter-evidence, which may not yet be available. Despite increasing interest in automatic fact-checking, it is still unclear if automated approaches can realistically refute harmful real-world misinformation. Here, we contrast and compare NLP fact-checking with how professional fact-checkers combat misinformation in the absence of counter-evidence. In our analysis, we show that, by design, existing NLP task definitions for fact-checking cannot refute misinformation as professional fact-checkers do for the majority of claims. We then define two requirements that the evidence in datasets must fulfill for realistic fact-checking: It must be (1) sufficient to refute the claim and (2) not leaked from existing fact-checking articles. We survey existing fact-checking datasets and find that all of them fail to satisfy both criteria. Finally, we perform experiments to demonstrate that models trained on a large-scale fact-checking dataset rely on leaked evidence, which makes them unsuitable in real-world scenarios. Taken together, we show that current NLP fact-checking cannot realistically combat real-world misinformation because it depends on unrealistic assumptions about counter-evidence in the data.

Related papers

• Grounding Fallacies Misrepresenting Scientific Publications in Evidence [84.32990746227385]
  We introduce MissciPlus, an extension of the fallacy detection dataset Missci. MissciPlus builds on Missci by grounding the applied fallacies in real-world passages from misrepresented studies. MissciPlus is the first logical fallacy dataset which pairs the real-world misrepresented evidence with incorrect claims.
  arXiv  Detail & Related papers  (2024-08-23T03:16:26Z)
• Missci: Reconstructing Fallacies in Misrepresented Science [84.32990746227385]
  Health-related misinformation on social networks can lead to poor decision-making and real-world dangers. Missci is a novel argumentation theoretical model for fallacious reasoning. We present Missci as a dataset to test the critical reasoning abilities of large language models.
  arXiv  Detail & Related papers  (2024-06-05T12:11:10Z)
• Credible, Unreliable or Leaked?: Evidence Verification for Enhanced Automated Fact-checking [11.891881050619457]
  "CREDible, Unreliable or LEaked" dataset consists of 91,632 articles classified as Credible, Unreliable and Fact checked (Leaked) "EVidence VERification Network (EVVER-Net) trained on CREDULE to detect leaked and unreliable evidence in both short and long texts" "EVVER-Net can demonstrate impressive performance of up to 91.5% and 94.4% accuracy"
  arXiv  Detail & Related papers  (2024-04-29T13:47:04Z)
• Generating Literal and Implied Subquestions to Fact-check Complex Claims [64.81832149826035]
  We focus on decomposing a complex claim into a comprehensive set of yes-no subquestions whose answers influence the veracity of the claim. We present ClaimDecomp, a dataset of decompositions for over 1000 claims. We show that these subquestions can help identify relevant evidence to fact-check the full claim and derive the veracity through their answers.
  arXiv  Detail & Related papers  (2022-05-14T00:40:57Z)
• Synthetic Disinformation Attacks on Automated Fact Verification Systems [53.011635547834025]
  We explore the sensitivity of automated fact-checkers to synthetic adversarial evidence in two simulated settings. We show that these systems suffer significant performance drops against these attacks. We discuss the growing threat of modern NLG systems as generators of disinformation.
  arXiv  Detail & Related papers  (2022-02-18T19:01:01Z)
• FaVIQ: FAct Verification from Information-seeking Questions [77.7067957445298]
  We construct a large-scale fact verification dataset called FaVIQ using information-seeking questions posed by real users. Our claims are verified to be natural, contain little lexical bias, and require a complete understanding of the evidence for verification.
  arXiv  Detail & Related papers  (2021-07-05T17:31:44Z)
• Automatic Fake News Detection: Are Models Learning to Reason? [9.143551270841858]
  We investigate the relationship and importance of both claim and evidence. Surprisingly, we find on political fact checking datasets that most often the highest effectiveness is obtained by utilizing only the evidence. This highlights an important problem in what constitutes evidence in existing approaches for automatic fake news detection.
  arXiv  Detail & Related papers  (2021-05-17T09:34:03Z)
• The Role of Context in Detecting Previously Fact-Checked Claims [27.076320857009655]
  We focus on claims made in a political debate, where context really matters. We study the impact of modeling the context of the claim both on the source side, as well as on the target side, in the fact-checking explanation document.
  arXiv  Detail & Related papers  (2021-04-15T12:39:37Z)
• Explainable Automated Fact-Checking for Public Health Claims [11.529816799331979]
  We present the first study of explainable fact-checking for claims which require specific expertise. For our case study we choose the setting of public health. We explore two tasks: veracity prediction and explanation generation.
  arXiv  Detail & Related papers  (2020-10-19T23:51:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.