ClaimRank: Detecting Check-Worthy Claims in Arabic and English

Jaradat, Israa; Gencheva, Pepa; Barrón-Cedeño, Alberto; Màrquez, Lluı́s; Nakov, Preslav

doi:10.48550/arxiv.1804.07587

Cited by 3 publications

(5 citation statements)

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“…Furthermore, Media Bias/Fact Check (https://mediabiasfactcheck.com (accessed on 3 June 2023)) (MBFC) [46], Disinformation detector, Emergent (http://emergent.info (accessed on 3 June 2023)), Politifact (https://politifact.com (accessed on 3 June 2023)), Hoaxy (https://hoaxy.osome.iu.edu (accessed on 3 June 2023)), and Snopes (https:// snopes.com (accessed on 3 June 2023)) are sources that verify facts and methods used in online misinformation [47,48]. Facebook collaborates with fact-checking groups to limit and appreciate the impact of propaganda news [49]. Nevertheless, it keeps track of the diffusion of information from independent fact-checking organizations and low-credibility sources.…”

Section: Related Workmentioning

confidence: 99%

Robust Benchmark for Propagandist Text Detection and Mining High-Quality Data

et al. 2023

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Social media, fake news, and different propaganda strategies have all contributed to an increase in misinformation online during the past ten years. As a result of the scarcity of high-quality data, the present datasets cannot be used to train a deep-learning model, making it impossible to establish an identification. We used a natural language processing approach to the issue in order to create a system that uses deep learning to automatically identify propaganda in news items. To assist the scholarly community in identifying propaganda in text news, this study suggested the propaganda texts (ProText) library. Truthfulness labels are assigned to ProText repositories after being manually and automatically verified with fact-checking methods. Additionally, this study proposed using a fine-tuned Robustly Optimized BERT Pre-training Approach (RoBERTa) and word embedding using multi-label multi-class text classification. Through experimentation and comparative research analysis, we address critical issues and collaborate to discover answers. We achieved an evaluation performance accuracy of 90%, 75%, 68%, and 65% on ProText, PTC, TSHP-17, and Qprop, respectively. The big-data method, particularly with deep-learning models, can assist us in filling out unsatisfactory big data in a novel text classification strategy. We urge collaboration to inspire researchers to acquire, exchange datasets, and develop a standard aimed at organizing, labeling, and fact-checking.

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Section: Related Workmentioning

confidence: 99%

Robust Benchmark for Propagandist Text Detection and Mining High-Quality Data

et al. 2023

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“…Check-worthiness detection can be subdivided into three distinct domains: rumour detection on Twitter, check-worthiness detection in political speeches and debates, and citation needed detection on Wikipedia. A few studies have been done which attempt to create full systems for mining check-worthy statements, including the works of Konstantinovskiy et al (2018), ClaimRank (Jaradat et al, 2018), and ClaimBuster (Hassan et al, 2017). They develop full software systems consisting of relevant source material retrieval, check-worthiness classification, and dissemination to the public via end-user applications.…”

Section: Check-worthiness Detectionmentioning

confidence: 99%

“…Political Speeches For political speeches, the most studied datasets come from the Clef Check-That! shared task Elsayed et al, 2019) and ClaimRank (Jaradat et al, 2018). The data consist of transcripts of political debates and speeches where each sentence has been annotated by an independent news or factchecking organization for whether or not the statement should be checked for veracity.…”

Section: Check-worthiness Detectionmentioning

confidence: 99%

“…Political Speeches The dataset we adopted in the political speeches domain is the same as in Hansen et al (2019), consisting of 4 political speeches from the 2018 Clef CheckThat! competition and 3 political speeches from ClaimRank (Jaradat et al, 2018) (2 602 statements total). We performed a 7-fold cross-validation, using 6 splits as training data and 1 as test in our experimental setup.…”

Section: Datasetsmentioning

confidence: 99%

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Claim Check-Worthiness Detection as Positive Unlabelled Learning

Wright¹,

Augenstein²

2020

Findings of the Association for Computational Linguistics: EMNLP 2020

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As the first step of automatic fact checking, claim check-worthiness detection is a critical component of fact checking systems. There are multiple lines of research which study this problem: check-worthiness ranking from political speeches and debates, rumour detection on Twitter, and citation needed detection from Wikipedia. To date, there has been no structured comparison of these various tasks to understand their relatedness, and no investigation into whether or not a unified approach to all of them is achievable. In this work, we illuminate a central challenge in claim check-worthiness detection underlying all of these tasks, being that they hinge upon detecting both how factual a sentence is, as well as how likely a sentence is to be believed without verification. As such, annotators only mark those instances they judge to be clear-cut check-worthy. Our best performing method is a unified approach which automatically corrects for this using a variant of positive unlabelled learning that finds instances which were incorrectly labelled as not check-worthy. In applying this, we outperform the state of the art in two of the three tasks studied for claim check-worthiness detection in English.

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“…In the context of misinformation, techniques relying on external information, known as evidencebased approaches, try to compare the claims made in the post with the existing evidence with known veracity. While some studies begin by extacting claims that are worth checking from the post [43,44,51,58], most work assumes that claims are given as input and focus on determining the truthfulness of those claims.…”

Section: External Evidencementioning

confidence: 99%

Preserving Integrity in Online Social Networks

Halevy¹,

Ferrer²,

Ma³

et al. 2020

Preprint

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Online social networks provide a platform for sharing information and free expression. However, these networks are also used for malicious purposes, such as distributing misinformation and hate speech, selling illegal drugs, and coordinating sex trafficking or child exploitation. This paper surveys the state of the art in keeping online platforms and their users safe from such harm, also known as the problem of preserving integrity.This survey comes from the perspective of having to combat a broad spectrum of integrity violations at Facebook. We highlight the techniques that have been proven useful in practice and that deserve additional attention from the academic community. Instead of discussing the many individual violation types, we identify key aspects of the social-media eco-system, each of which is common to a wide variety violation types. Furthermore, each of these components represents an area for research and development, and the innovations that are found can be applied widely.

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ClaimRank: Detecting Check-Worthy Claims in Arabic and English

Cited by 3 publications

References 0 publications

Robust Benchmark for Propagandist Text Detection and Mining High-Quality Data

Robust Benchmark for Propagandist Text Detection and Mining High-Quality Data

Claim Check-Worthiness Detection as Positive Unlabelled Learning

Preserving Integrity in Online Social Networks

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