Predicting Stance Change Using Modular Architectures

Porco, Aldo; Goldwasser, Dan

doi:10.18653/v1/2020.coling-main.35

Cited by 2 publications

(3 citation statements)

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“…Stance detection gained a significant interest in recent years, e.g., (Somasundaran and Wiebe 2010;Walker et al 2012a;Sridhar et al 2015;Mohammad et al 2016;Derczynski et al 2017;Sobhani, Inkpen, and Zhu 2017;Joseph et al 2017;Li, Porco, and Goldwasser 2018;Porco and Goldwasser 2020;Conforti et al 2020), among many others. A comprehensive survey of the various settings, datasets, and computational approaches is provided in (Küc ¸ük and Can 2020).…”

Section: Related Workmentioning

confidence: 75%

“…Stance at the user level, sometimes referred to as 'aggregate' or 'collective' stance, is addressed by (Murakami and Raymond 2010;Walker et al 2012b;Yin et al 2012). A more nuanced relationship between the post and the user level is addressed by (Sridhar et al 2015;Li, Porco, and Goldwasser 2018;Benton and Dredze 2018;Conforti et al 2020;Porco and Goldwasser 2020). We follow this observation and report results on both post and user levels.…”

Section: Related Workmentioning

confidence: 99%

“…Leveraging the conversation structure was recently used by (Li, Porco, and Goldwasser 2018;Porco and Goldwasser 2020) to create a global representation based on authors interaction and the text. Stance-based rumor detection is explored by (Wei, Xu, and Mao 2019), considering the structure of the conversation, along with content.…”

Section: Related Workmentioning

confidence: 99%

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STEM: Unsupervised STructural EMbedding for Stance Detection

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Kozhukhov

Vilenchik

et al. 2022

AAAI

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Stance detection is an important task, supporting many downstream tasks such as discourse parsing and modeling the propagation of fake news, rumors, and science denial. In this paper, we propose a novel framework for stance detection. Our framework is unsupervised and domain-independent. Given a claim and a multi-participant discussion -- we construct the interaction network from which we derive topological embedding for each speaker. These speaker embedding enjoy the following property: speakers with the same stance tend to be represented by similar vectors, while antipodal vectors represent speakers with opposing stances. These embedding are then used to divide the speakers into stance-partitions. We evaluate our method on three different datasets from different platforms. Our method outperforms or is comparable with supervised models while providing confidence levels for its output. Furthermore, we demonstrate how the structural embedding relate to the valence expressed by the speakers. Finally, we discuss some limitations inherent to the framework.

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Section: Related Workmentioning

confidence: 75%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

STEM: Unsupervised STructural EMbedding for Stance Detection

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Kozhukhov

Vilenchik

et al. 2022

AAAI

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Scientia Potentia Est—On the Role of Knowledge in Computational Argumentation

Lauscher

Wachsmuth

Gurevych

et al. 2022

Transactions of the Association for Computational Linguistics

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Despite extensive research efforts in recent years, computational argumentation (CA) remains one of the most challenging areas of natural language processing. The reason for this is the inherent complexity of the cognitive processes behind human argumentation, which integrate a plethora of different types of knowledge, ranging from topic-specific facts and common sense to rhetorical knowledge. The integration of knowledge from such a wide range in CA requires modeling capabilities far beyond many other natural language understanding tasks. Existing research on mining, assessing, reasoning over, and generating arguments largely acknowledges that much more knowledge is needed to accurately model argumentation computationally. However, a systematic overview of the types of knowledge introduced in existing CA models is missing, hindering targeted progress in the field. Adopting the operational definition of knowledge as any task-relevant normative information not provided as input, the survey paper at hand fills this gap by (1) proposing a taxonomy of types of knowledge required in CA tasks, (2) systematizing the large body of CA work according to the reliance on and exploitation of these knowledge types for the four main research areas in CA, and (3) outlining and discussing directions for future research efforts in CA.

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Predicting Stance Change Using Modular Architectures

Cited by 2 publications

References 24 publications

STEM: Unsupervised STructural EMbedding for Stance Detection

STEM: Unsupervised STructural EMbedding for Stance Detection

Scientia Potentia Est—On the Role of Knowledge in Computational Argumentation

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