Connecting the Dots Between Fact Verification and Fake News Detection

Li, Qifei; Zhou, Wangchunshu

doi:10.18653/v1/2020.coling-main.165

Cited by 20 publications

(6 citation statements)

References 21 publications

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“…A simple source for obtaining evidence to verify the accuracy of information is the web. In several works, such as [14][15][16]52], the authors used web search engines, such as Google or Bing, to collect relevant articles; they also used scraped information, such as an external feature to build a fake news classifier. As discussed in Section 2.1, this web-based feature is motivated by the behaviors of real-life users.…”

Section: Methods Based On External Featuresmentioning

confidence: 99%

Multiverse: Multilingual Evidence for Fake News Detection

2023

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The rapid spread of deceptive information on the internet can have severe and irreparable consequences. As a result, it is important to develop technology that can detect fake news. Although significant progress has been made in this area, current methods are limited because they focus only on one language and do not incorporate multilingual information. In this work, we propose Multiverse—a new feature based on multilingual evidence that can be used for fake news detection and improve existing approaches. Our hypothesis that cross-lingual evidence can be used as a feature for fake news detection is supported by manual experiments based on a set of true (legit) and fake news. Furthermore, we compared our fake news classification system based on the proposed feature with several baselines on two multi-domain datasets of general-topic news and one fake COVID-19 news dataset, showing that (in combination with linguistic features) it yields significant improvements over the baseline models, bringing additional useful signals to the classifier.

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Section: Methods Based On External Featuresmentioning

confidence: 99%

Multiverse: Multilingual Evidence for Fake News Detection

2023

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“…Fake news detection is commonly considered as a binary classification problem, with the goal of accurately predicting a given news article as real or fake. Among existing studies, content-based methods extract semantic patterns from the news content using a wide range of deep learning architectures that include RNNs [29] and pre-trained language models (PLMs) [20,28]. Some methods also guide model prediction with auxiliary information including knowledge bases [5,10,15,45], evidence from external sources [3,34,47], visual information [2,32,42,51], and signals from the news environment [33].…”

Section: Related Work 21 Fake News Detectionmentioning

confidence: 99%

DECOR: Degree-Corrected Social Graph Refinement for Fake News Detection

Hooi

2023

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

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Recent efforts in fake news detection have witnessed a surge of interest in using graph neural networks (GNNs) to exploit rich social context. Existing studies generally leverage fixed graph structures, assuming that the graphs accurately represent the related social engagements. However, edge noise remains a critical challenge in real-world graphs, as training on suboptimal structures can severely limit the expressiveness of GNNs. Despite initial efforts in graph structure learning (GSL), prior works often leverage node features to update edge weights, resulting in heavy computational costs that hinder the methods' applicability to large-scale social graphs. In this work, we approach the fake news detection problem with a novel aspect of social graph refinement. We find that the degrees of news article nodes exhibit distinctive patterns, which are indicative of news veracity. Guided by this, we propose DECOR, a novel application of Degree-Corrected Stochastic Blockmodels to the fake news detection problem. Specifically, we encapsulate our empirical observations into a lightweight social graph refinement component that iteratively updates the edge weights via a learnable degree correction mask, which allows for joint optimization with a GNN-based detector. Extensive experiments on two real-world benchmarks validate the effectiveness and efficiency of DECOR. 1 CCS CONCEPTS• Information systems → Data mining; • Computing methodologies → Neural networks.

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“…Another quite strong signal can be additional information extracted from the Web. In (Popat et al, 2017;Karadzhov et al, 2017;Ghanem et al, 2018;Li and Zhou, 2020) the authors referred to the Web search (Google or Bing) to collect relevant articles and use such scraped information as an external feature to build a fake news classifier.…”

Section: Related Workmentioning

confidence: 99%

Cross-lingual Evidence Improves Monolingual Fake News Detection

Dementieva¹,

Panchenko²

2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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Misleading information spreads on the Internet at an incredible speed, which can lead to irreparable consequences in some cases. Therefore, it is becoming essential to develop fake news detection technologies. While substantial work has been done in this direction, one of the limitations of the current approaches is that these models are focused only on one language and do not use multilingual information. In this work, we propose a new technique based on cross-lingual evidence (CE) that can be used for fake news detection and improve existing approaches. The hypothesis of the usage of cross-lingual evidence as a feature for fake news detection is confirmed, firstly, by manual experiment based on a set of known true and fake news. Besides, we compared our fake news classification system based on the proposed feature with several strong baselines on two multi-domain datasets of generaltopic news and one newly fake COVID-19 news dataset showing that combining crosslingual evidence with strong baselines such as RoBERTa yields significant improvements in fake news detection.

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Connecting the Dots Between Fact Verification and Fake News Detection

Cited by 20 publications

References 21 publications

Multiverse: Multilingual Evidence for Fake News Detection

Multiverse: Multilingual Evidence for Fake News Detection

DECOR: Degree-Corrected Social Graph Refinement for Fake News Detection

Cross-lingual Evidence Improves Monolingual Fake News Detection

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