Fact-Checking Meets Fauxtography: Verifying Claims About Images

Zlatkova, Dimitrina; Nakov, Preslav; Koychev, Ivan

doi:10.18653/v1/d19-1216

Cited by 63 publications

(48 citation statements)

References 22 publications

(19 reference statements)

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“…A number of recent works in automated factchecking look at various formulations of factchecking and its analogous tasks (Ferreira and Vlachos, 2016;Hassan et al, 2017;Zlatkova et al, 2019). In this paper, we choose to focus on the two specific aspects of concern to us, which have not been thoroughly explored in the literature.…”

Section: Related Workmentioning

confidence: 99%

Explainable Automated Fact-Checking for Public Health Claims

Kotonya¹,

Toni²

2020

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

101

115

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Fact-checking is the task of verifying the veracity of claims by assessing their assertions against credible evidence. The vast majority of fact-checking studies focus exclusively on political claims. Very little research explores fact-checking for other topics, specifically subject matters for which expertise is required. 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. To support this case study we construct a new dataset PUBHEALTH of 11.8K claims accompanied by journalist crafted, gold standard explanations (i.e., judgments) to support the fact-check labels for claims 1 . We explore two tasks: veracity prediction and explanation generation. We also define and evaluate, with humans and computationally, three coherence properties of explanation quality. Our results indicate that, by training on in-domain data, gains can be made in explainable, automated fact-checking for claims which require specific expertise.Table 9: These are the four standardized labels we defined for veracity prediction (left) and lists (right) of the original fact-checking labels provided by the fact-checking and news review websites we scraped, mapped to our four standardized labels .

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Section: Related Workmentioning

confidence: 99%

Explainable Automated Fact-Checking for Public Health Claims

Kotonya¹,

Toni²

2020

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

101

115

View full text Add to dashboard Cite

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“…For example, there are 4-way classification of news documents (Rashkin et al, 2017), and 6way classification of short statements (Wang, 2017). There are also sentence-level fact checking problems with various genres of evidence, including natural language sentences from Wikipedia (Thorne et al, 2018), semi-structured tables (Chen et al, 2020), and images (Zlatkova et al, 2019;Nakamura et al, 2019). Our work studies propaganda detection, a fine-grained problem that requires tokenlevel prediction over 18 fine-grained propaganda techniques.…”

Section: Related Workmentioning

confidence: 99%

Leveraging Declarative Knowledge in Text and First-Order Logic for Fine-Grained Propaganda Detection

Wang

Tang

Duan

et al. 2020

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

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We study the detection of propagandistic text fragments in news articles. Instead of merely learning from input-output datapoints in training data, we introduce an approach to inject declarative knowledge of fine-grained propaganda techniques. Specifically, we leverage the declarative knowledge expressed in both first-order logic and natural language. The former refers to the logical consistency between coarse-and fine-grained predictions, which is used to regularize the training process with propositional Boolean expressions. The latter refers to the literal definition of each propaganda technique, which is utilized to get class representations for regularizing the model parameters. We conduct experiments on Propaganda Techniques Corpus, a large manually annotated dataset for fine-grained propaganda detection. Experiments show that our method achieves superior performance, demonstrating that leveraging declarative knowledge can help the model to make more accurate predictions.

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“…At the claim-level, fact-checking and rumor detection have been primarily addressed using information extracted from social media, i.e., based on how users comment on the target claim [3,4,5,6,7,8,9]. The Web has also been used as an information source [10,11,12,13,14,15].…”

Section: Related Workmentioning

confidence: 99%

Detecting Deception in Political Debates Using Acoustic and Textual Features

Kopev

Ali²,

Koychev

et al. 2019

2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)

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We present work on deception detection, where, given a spoken claim, we aim to predict its factuality. While previous work in the speech community has relied on recordings from staged setups where people were asked to tell the truth or to lie and their statements were recorded, here we use real-world political debates. Thanks to the efforts of fact-checking organizations, it is possible to obtain annotations for statements in the context of a political discourse as true, half-true, or false. Starting with such data from the CLEF-2018 CheckThat! Lab, which was limited to text, we performed alignment to the corresponding videos, thus producing a multimodal dataset. We further developed a multimodal deep-learning architecture for the task of deception detection, which yielded sizable improvements over the state of the art for the CLEF-2018 Lab task 2. Our experiments show that the use of the acoustic signal consistently helped to improve the performance compared to using textual and metadata features only, based on several different evaluation measures. We release the new dataset to the research community, hoping to help advance the overall field of multimodal deception detection.

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Fact-Checking Meets Fauxtography: Verifying Claims About Images

Cited by 63 publications

References 22 publications

Explainable Automated Fact-Checking for Public Health Claims

Explainable Automated Fact-Checking for Public Health Claims

Leveraging Declarative Knowledge in Text and First-Order Logic for Fine-Grained Propaganda Detection

Detecting Deception in Political Debates Using Acoustic and Textual Features

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