Attributed Multi-Relational Attention Network for Fact-checking URL Recommendation

You, Di; Vo, Nguyen; Lee, Kyumin; Liu, Qiang

doi:10.1145/3357384.3358006

Cited by 19 publications

(9 citation statements)

References 37 publications

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“…Our paper deviates from these work since we aim to find FC-articles given multimodal fake news in social media posts. As our goal is to increase users' awareness of verified news, studies about fact-checkers Lee, 2018, 2019;You et al, 2019) are close to ours.…”

Section: Related Worksupporting

confidence: 65%

Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News

Vo¹,

Lee²

2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Although many fact-checking systems have been developed in academia and industry, fake news is still proliferating on social media. These systems mostly focus on fact-checking but usually neglect online users who are the main drivers of the spread of misinformation. How can we use fact-checked information to improve users' consciousness of fake news to which they are exposed? How can we stop users from spreading fake news? To tackle these questions, we propose a novel framework to search for fact-checking articles, which address the content of an original tweet (that may contain misinformation) posted by online users. The search can directly warn fake news posters and online users (e.g. the posters' followers) about misinformation, discourage them from spreading fake news, and scale up verified content on social media. Our framework uses both text and images to search for fact-checking articles, and achieves promising results on real-world datasets. Our code and datasets are released at https:// github.com/nguyenvo09/EMNLP2020.

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Section: Related Worksupporting

confidence: 65%

Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News

Vo¹,

Lee²

2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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“…content moderation on social media sites). In machine-learning-based methods, researchers mainly used linguistics and textual content (Zellers et al, 2019;Zhao et al, 2015;Wang, 2017;Shu et al, 2019), temporal spreading patterns , network structures Vo and Lee, 2018;You et al, 2019), users' feedbacks Shu et al, 2019) and multimodal signals (Gupta et al, 2013;Vo and Lee, 2020b). Recently, researchers focus on fact-checking claims based on evidence from different sources.…”

Section: Related Workmentioning

confidence: 99%

Hierarchical Multi-head Attentive Network for Evidence-aware Fake News Detection

Vo¹,

Lee²

2021

Preprint

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The widespread of fake news and misinformation in various domains ranging from politics, economics to public health has posed an urgent need to automatically fact-check information. A recent trend in fake news detection is to utilize evidence from external sources. However, existing evidence-aware fake news detection methods focused on either only word-level attention or evidence-level attention, which may result in suboptimal performance. In this paper, we propose a Hierarchical Multihead Attentive Network to fact-check textual claims. Our model jointly combines multi-head word-level attention and multihead document-level attention, which aid explanation in both word-level and evidencelevel. Experiments on two real-word datasets show that our model outperforms seven stateof-the-art baselines. Improvements over baselines are from 6% to 18%. Our source code and datasets are released at https:// github.com/nguyenvo09/EACL2021.

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“…In the last few years, the research community has been looking at automatic check-worthiness predictions [15,49], at truthfulness detection/credibility assessments [5,12,24,33,34,39], and at developing fact-checking URL recommender systems and text generation models to mitigate the impact of fake news in social media [51,52,56]. In this section we focus on the literature that explored crowdsourcing methodologies to collect truthfulness judgments, the different judgment scales that have been used so far, and the relation between assessors' bias and the data they produce.…”

Section: Related Workmentioning

confidence: 99%

Can The Crowd Identify Misinformation Objectively? The Effects of Judgment Scale and Assessor's Background

Roitero,

Soprano,

Fan

et al. 2020

Preprint

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Truthfulness judgments are a fundamental step in the process of fighting misinformation, as they are crucial to train and evaluate classifiers that automatically distinguish true and false statements. Usually such judgments are made by experts, like journalists for political statements or medical doctors for medical statements. In this paper, we follow a different approach and rely on (non-expert) crowd workers. This of course leads to the following research question: Can crowdsourcing be reliably used to assess the truthfulness of information and to create large-scale labeled collections for information credibility systems? To address this issue, we present the results of an extensive study based on crowdsourcing: we collect thousands of truthfulness assessments over two datasets, and we compare expert judgments with crowd judgments, expressed on scales with various granularity levels. We also measure the political bias and the cognitive background of the workers, and quantify their effect on the reliability of the data provided by the crowd.

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Attributed Multi-Relational Attention Network for Fact-checking URL Recommendation

Cited by 19 publications

References 37 publications

Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News

Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News

Hierarchical Multi-head Attentive Network for Evidence-aware Fake News Detection

Can The Crowd Identify Misinformation Objectively? The Effects of Judgment Scale and Assessor's Background

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