Unsupervised stance classification in online debates

Ghosh, Subrata; Konjengbam, Anand; Rajanala, Sailaja; Reddy, A. Bharath Kumar; Singh, Manish

doi:10.1145/3152494.3152497

Cited by 9 publications

(3 citation statements)

References 12 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 Worksupporting

confidence: 85%

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 Worksupporting

confidence: 85%

STEM: Unsupervised STructural EMbedding for Stance Detection

Pick

Kozhukhov

Vilenchik

et al. 2022

AAAI

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show abstract

“…Previous approaches focus on learning topic-specific models to classify stances of related claims with machine learning models [2,10,18] as well as deep learning models [20,7,8,15,16,24]. Previous work has also looked at doing stance classification at challenging situations such as zero-shot [1] and unsupervised settings [19,9,11]. Since stance classification has been thought of as a subtask of sentiment analysis [12], the use of sentiment lexicon is popular in previous work.…”

Section: Related Workmentioning

confidence: 99%

Distant finetuning with discourse relations for stance classification

Jin¹,

Xu²,

Song³

et al. 2022

Preprint

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Approaches for the stance classification task, an important task for understanding argumentation in debates and detecting fake news, have been relying on models which deal with individual debate topics. In this paper, in order to train a system independent from topics, we propose a new method to extract data with silver labels from raw text to finetune a model for stance classification. The extraction relies on specific discourse relation information, which is shown as a reliable and accurate source for providing stance information. We also propose a 3-stage training framework where the noisy level in the data used for finetuning decreases over different stages going from the most noisy to the least noisy. Detailed experiments show that the automatically annotated dataset as well as the 3-stage training help improve model performance in stance classification. Our approach ranks 1 st among 26 competing teams in the stance classification track of the NLPCC 2021 shared task Argumentative Text Understanding for AI Debater, which confirms the effectiveness of our approach.

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“…Likewise, Kyaw [23] combines tf-idf weights with POS-tags to build the text representation for stance detection. Ghosh and Anand [24] propose a two stage method which firstly detect the argumentative posts from review corpus and then detect the stance for argumentative ones.…”

Section: ) Textual Content Based Approachesmentioning

confidence: 99%

A Survey on Opinion Mining: From Stance to Product Aspect

Wang¹,

Zhou²,

Jiang³

et al. 2019

IEEE Access

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With the prevalence of social media and online forum, opinion mining, aiming at analyzing and discovering the latent opinion in user-generated reviews on the Internet, has become a hot research topic. This survey focuses on two important subtasks in this field, stance detection and product aspect mining, both of which can be formalized as the problem of the triple target, aspect, opinion extraction. In this paper, we first introduce the general framework of opinion mining and describe the evaluation metrics. Then, the methodologies for stance detection on different sources, such as online forum and social media are discussed. After that, approaches for product aspect mining are categorized into three main groups which are corpus level aspect extraction, corpus level aspect, and opinion mining, and document level aspect and opinion mining based on the processing units and tasks. And then we discuss the challenges and possible solutions. Finally, we summarize the evolving trend of the reviewed methodologies and conclude the survey. INDEX TERMS Opinion mining, stance detection, product aspect mining, topic model, deep neural network.

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Unsupervised stance classification in online debates

Cited by 9 publications

References 12 publications

STEM: Unsupervised STructural EMbedding for Stance Detection

STEM: Unsupervised STructural EMbedding for Stance Detection

Distant finetuning with discourse relations for stance classification

A Survey on Opinion Mining: From Stance to Product Aspect

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