It Takes Nine to Smell a Rat: Neural Multi-Task Learning for Check-Worthiness Prediction

Vasileva, Slavena; Atanasova, Pepa; Màrquez, Lluı́s; Barrón-Cedeño, Alberto; Nakov, Preslav

doi:10.26615/978-954-452-056-4_141

Cited by 31 publications

(21 citation statements)

References 46 publications

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“…Check-Worthiness Estimation Notable work in this direction includes context-aware approaches to detect check-worthy claims in political debates (Gencheva et al, 2017), using various patterns to find factual claims (Ennals et al, 2010), multitask learning (Vasileva et al, 2019b), and a variety of other approaches used by the participants of the CLEF CheckThat! labs' shared tasks on checkworthiness (Nakov et al, 2018;Elsayed et al, 2019b,a;Vasileva et al, 2019a).…”

Section: Related Workmentioning

confidence: 99%

That is a Known Lie: Detecting Previously Fact-Checked Claims

Shaar¹,

Babulkov²,

Martino³

et al. 2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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The recent proliferation of "fake news" has triggered a number of responses, most notably the emergence of several manual fact-checking initiatives. As a result and over time, a large number of fact-checked claims have been accumulated, which increases the likelihood that a new claim in social media or a new statement by a politician might have already been factchecked by some trusted fact-checking organization, as viral claims often come back after a while in social media, and politicians like to repeat their favorite statements, true or false, over and over again. As manual fact-checking is very time-consuming (and fully automatic fact-checking has credibility issues), it is important to try to save this effort and to avoid wasting time on claims that have already been fact-checked. Interestingly, despite the importance of the task, it has been largely ignored by the research community so far. Here, we aim to bridge this gap. In particular, we formulate the task and we discuss how it relates to, but also differs from, previous work. We further create a specialized dataset, which we release to the research community. Finally, we present learning-to-rank experiments that demonstrate sizable improvements over state-of-the-art retrieval and textual similarity approaches.

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Section: Related Workmentioning

confidence: 99%

That is a Known Lie: Detecting Previously Fact-Checked Claims

Shaar¹,

Babulkov²,

Martino³

et al. 2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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“…In recent years, several automated fact-checking techniques [45,40,16,6,29,5,41] have been developed to reduce the manual fact-checking overhead. For instance, Wang et al [45] created a dataset of short statements from several political speeches and designed a technique to detect fake claims by analyzing linguistic patterns in the speeches.…”

Section: Automated Fact-checkingmentioning

confidence: 99%

COSMOS: Catching Out-of-Context Misinformation with Self-Supervised Learning

Aneja¹,

Bregler²,

Nießner³

2021

Preprint

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Figure 1: Our method takes as input an image and two captions from different sources, and we predict whether the image has been used out of context. Here, both captions align with same object(s) in the image; i.e., Donald Trump and Angela Merkel on the left and President Obama and Dr. Fauci on the right. If the two captions are semantically different from each other (right), we consider them as out of context; otherwise not (left). Our method automatically detects such image use.

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“…Note that political debates and speeches require modeling the context of the target sentence to classify. Indeed, context was a major focus for most research in the debates domain (Gencheva et al, 2017;Patwari et al, 2017;Vasileva et al, 2019;. For example, Vasileva et al (2019) modeled context in a multi-task learning neural network that predicts whether a sentence would be selected for fact-checking by each fact-checking organization (from a set of nine such organizations).…”

Section: Check-worthiness Estimationmentioning

confidence: 99%

A Second Pandemic? Analysis of Fake News About COVID-19 Vaccines in Qatar

Nakov¹,

Alam²,

Shaar³

et al. 2021

Proceedings of the Conference Recent Advances in Natural Language Processing - Deep Learning for Natural Language Processing Me

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While COVID-19 vaccines are finally becoming widely available, a second pandemic that revolves around the circulation of anti-vaxxer "fake news" may hinder efforts to recover from the first one. With this in mind, we performed an extensive analysis of Arabic and English tweets about COVID-19 vaccines, with focus on messages originating from Qatar. We found that Arabic tweets contain a lot of false information and rumors, while English tweets are mostly factual. However, English tweets are much more propagandistic than Arabic ones. In terms of propaganda techniques, about half of the Arabic tweets express doubt, and 1/5 use loaded language, while English tweets are abundant in loaded language, exaggeration, fear, name-calling, doubt, and flag-waving. Finally, in terms of framing, Arabic tweets adopt a health and safety perspective, while in English economic concerns dominate.

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It Takes Nine to Smell a Rat: Neural Multi-Task Learning for Check-Worthiness Prediction

Cited by 31 publications

References 46 publications

That is a Known Lie: Detecting Previously Fact-Checked Claims

That is a Known Lie: Detecting Previously Fact-Checked Claims

COSMOS: Catching Out-of-Context Misinformation with Self-Supervised Learning

A Second Pandemic? Analysis of Fake News About COVID-19 Vaccines in Qatar

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