A Survey on Stance Detection for Mis- and Disinformation Identification

Hardalov, Momchil; Arora, Arnav; Nakov, Preslav; Augenstein, Isabelle

doi:10.18653/v1/2022.findings-naacl.94

Cited by 48 publications

(22 citation statements)

References 109 publications

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“…Stance detection is typically formulated as a classification task (or ordinal regression) over each piece of retrieved evidence. Note that some works formulate stance detection as an independent task (Hanselowski et al, 2018;Hardalov et al, 2021).…”

Section: Evidence Retrievalmentioning

confidence: 99%

The State of Human-centered NLP Technology for Fact-checking

Das,

Liu,

Kovatchev

et al. 2023

Preprint

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Misinformation threatens modern society by promoting distrust in science, changing narratives in public health, heightening social polarization, and disrupting democratic elections and financial markets, among a myriad of other societal harms. To address this, a growing cadre of professional fact-checkers and journalists provide high-quality investigations into purported facts. However, these largely manual efforts have struggled to match the enormous scale of the problem. In response, a growing body of Natural Language Processing (NLP) technologies have been proposed for more scalable fact-checking. Despite tremendous growth in such research, however, practical adoption of NLP technologies for fact-checking still remains in its infancy today.In this work, we review the capabilities and limitations of the current NLP technologies for fact-checking. Our particular focus is to further chart the design space for how these technologies can be harnessed and refined in order to better meet the needs of human fact-checkers. To do so, we review key aspects of NLP-based fact-checking: task formulation, dataset construction, modeling, and human-centered strategies, such as explainable models and human-in-the-loop approaches. Next, we review the efficacy of applying NLP-based fact-checking tools to assist human fact-checkers. We recommend that future research include collaboration with fact-checker stakeholders early on in NLP research, as well as incorporation of human-centered design practices in model development, in order to further guide technology development for human use and practical adoption. Finally, we advocate for more research on benchmark development supporting extrinsic evaluation of human-centered fact-checking technologies.

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Section: Evidence Retrievalmentioning

confidence: 99%

The State of Human-centered NLP Technology for Fact-checking

Das,

Liu,

Kovatchev

et al. 2023

Preprint

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“…A myriad of studies have investigated detecting the stance towards claims to identify its veracity [38]. Some focusing on detecting the stance of conversation threads in social media [19,20,39], and others on the stance of news articles [21,22,40,41].…”

Section: Stance Detection For Claim Verificationmentioning

confidence: 99%

Are Authorities Denying or Supporting? Detecting Stance of Authorities Towards Rumors in Twitter

Haouari,

Elsayed

2023

Preprint

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Several studies examined the leverage of the stance in conversational threads or news articles as a signal for rumor verification. However, none of these studies leveraged the stance of trusted authorities. In this work, we define the task of detecting the stance of authorities towards rumors in Twitter, i.e., whether a tweet from an authority supports the rumor, denies it, or neither. We believe the task is useful to augment the sources of evidence exploited by existing rumor verification models. We construct and release the first Authority STance towards Rumors (AuSTR) dataset, where evidence is retrieved from authority timelines in Arabic Twitter. The collection comprises 811 (rumor tweet, authority tweet) pairs relevant to 292 unique rumors. Due to the relatively limited size of our dataset, we explore the adequacy of existing Arabic datasets of stance towards claims in training BERT-based models for our task, and the effect of augmenting AuSTR with those datasets. Our experiments show that, despite its limited size, a model trained solely on AuSTR with a class-balanced focus loss exhibits a comparable performance to the best studied combination of existing datasets augmented with AuSTR, achieving a performance of 0.84 macro-F1 and 0.78 F1 on debunking tweets. The results indicate that AuSTR can be sufficient for our task without the need for augmenting it with existing stance datasets. Finally, we conduct a thorough failure analysis to gain insights for the future directions on the task.

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“…Furthermore, the utilization of users' stances have shown promising results in the verification of rumors, and a large variety of approaches have been adopted towards exploiting users' stances. In spite of the existing surveys on rumor detection systems [25], [26], [27], a specifically comprehensive review on the recent methods, features, datasets, and challenges for rumor stance classification has not yet been reported. To fill this gap, this paper concerns the state-ofthe-art in rumor verification systems by exploiting wisdom of the crowd.…”

Section: Motivationmentioning

confidence: 99%

Rumor Stance Classification in Online Social Networks: The State-of-the-Art, Prospects, and Future Challenges

et al. 2022

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The emergence of the Internet as a ubiquitous technology has facilitated the rapid evolution of social media as the leading virtual platform for communication, content sharing, and information dissemination. In spite of revolutionizing the way news is delivered to people, this technology has also brought along with itself inevitable demerits. One such drawback is the spread of rumors expedited by social media platforms, which may provoke doubt and fear. Therefore, it is essential to debunk rumors before their widespread use. Over the years, many studies have been conducted to develop effective rumor verification systems. One aspect of such studies focuses on rumor stance classification, which involves the task of utilizing user viewpoints regarding a rumorous post to better predict the veracity of a rumor. Relying on user stances in rumor verification has gained significant importance, for it has resulted in significant improvements in the model performance. In this paper, we conduct a comprehensive literature review of rumor stance classification in complex online social networks (OSNs). In particular, we present a thorough description of these approaches and compare their performances. Moreover, we introduce multiple datasets available for this purpose and highlight their limitations. Finally, challenges and future directions are discussed to stimulate further relevant research efforts.

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A Survey on Stance Detection for Mis- and Disinformation Identification

Cited by 48 publications

References 109 publications

The State of Human-centered NLP Technology for Fact-checking

The State of Human-centered NLP Technology for Fact-checking

Are Authorities Denying or Supporting? Detecting Stance of Authorities Towards Rumors in Twitter

Rumor Stance Classification in Online Social Networks: The State-of-the-Art, Prospects, and Future Challenges

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