Debate Stance Classification Using Word Embeddings

Konjengbam, Anand; Ghosh, Subrata; Kumar, Nagendra; Singh, Manish

doi:10.1007/978-3-319-98539-8_29

Cited by 7 publications

(4 citation statements)

References 8 publications

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“…Kobbe et al (2020) classify stance based on frequently used argumentation structures. Other unsupervised approaches include the use of syntactic rules for extraction of topic and aspect pairs (Ghosh et al 2018) or by extracting aspect-polarity-target information (Konjengbam et al 2018). These approaches are language dependant, often use external resources, and are not easily adapted to different domains and communities that present a variety of discussion norms.…”

Section: Related Workmentioning

confidence: 99%

STEM: Unsupervised STructural EMbedding for Stance Detection

Pick

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: 99%

STEM: Unsupervised STructural EMbedding for Stance Detection

Pick

Kozhukhov

Vilenchik

et al. 2022

AAAI

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“…support, against, for or neutral) depend on the precise problem. The task, which concerns a diverse range of domains, is studied in such varied areas as political debates [24,25], student essays [26], online forum debates [27] or even internal company discussions [28,29]. A great deal of work in opinion mining has been devoted to detect the stance of tweets or other types of short texts as rumors [30] or microblogging statements.…”

Section: • Stance Detection Overviewmentioning

confidence: 99%

HeadlineStanceChecker: Exploiting summarization to detect headline disinformation

Sepúlveda-Torres

Vicente

Saquete

et al. 2021

Journal of Web Semantics

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“…Stance can be binary ("for" or "against"), or be described by more fine-grained types (supporting, contradicting, questioning, or commenting) [28], which is what we employ in this work. Stance classification of social media postings has been studied mostly in the context of online marketing [35] and political discourse and rumors [63].…”

Section: Indicator Extraction Techniquesmentioning

confidence: 99%

SciLens: Evaluating the Quality of Scientific News Articles Using Social Media and Scientific Literature Indicators

Smeros

Castillo

Aberer

2019

The World Wide Web Conference

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This paper describes, develops, and validates SciLens, a method to evaluate the quality of scientific news articles. The starting point for our work are structured methodologies that define a series of quality aspects for manually evaluating news. Based on these aspects, we describe a series of indicators of news quality. According to our experiments, these indicators help non-experts evaluate more accurately the quality of a scientific news article, compared to nonexperts that do not have access to these indicators. Furthermore, SciLens can also be used to produce a completely automated quality score for an article, which agrees more with expert evaluators than manual evaluations done by non-experts. One of the main elements of SciLens is the focus on both content and context of articles, where context is provided by (1) explicit and implicit references on the article to scientific literature, and (2) reactions in social media referencing the article. We show that both contextual elements can be valuable sources of information for determining article quality. The validation of SciLens, done through a combination of expert and non-expert annotation, demonstrates its effectiveness for both semi-automatic and automatic quality evaluation of scientific news.

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Debate Stance Classification Using Word Embeddings

Cited by 7 publications

References 8 publications

STEM: Unsupervised STructural EMbedding for Stance Detection

STEM: Unsupervised STructural EMbedding for Stance Detection

HeadlineStanceChecker: Exploiting summarization to detect headline disinformation

SciLens: Evaluating the Quality of Scientific News Articles Using Social Media and Scientific Literature Indicators

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