Improving Implicit Stance Classification in Tweets Using Word and Sentence Embeddings

Schaefer, Robin; Stede, Manfred

doi:10.1007/978-3-030-30179-8_26

Cited by 5 publications

(2 citation statements)

References 10 publications

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“…For example, in the utterance "I am supportive of A, but I'm totally against B", the author expresses a supporting stance towards target A and an opposing stance towards target B. The target may be implicit and not always directly mentioned in the text (Schaefer & Stede, 2019). The target may be implicitly referred to Sobhani et al (2019), or only aspects of it may be mentioned (Bar-Haim et al, 2017).…”

Section: Stance Utterancementioning

confidence: 99%

Capturing stance dynamics in social media: open challenges and research directions

Alkhalifa

Zubiaga

2022

Int J Digit Humanities

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Social media platforms provide a goldmine for mining public opinion on issues of wide societal interest and impact. Opinion mining is a problem that can be operationalised by capturing and aggregating the stance of individual social media posts as supporting, opposing or being neutral towards the issue at hand. While most prior work in stance detection has investigated datasets that cover short periods of time, interest in investigating longitudinal datasets has recently increased. Evolving dynamics in linguistic and behavioural patterns observed in new data require adapting stance detection systems to deal with the changes. In this survey paper, we investigate the intersection between computational linguistics and the temporal evolution of human communication in digital media. We perform a critical review of emerging research considering dynamics, exploring different semantic and pragmatic factors that impact linguistic data in general, and stance in particular. We further discuss current directions in capturing stance dynamics in social media. We discuss the challenges encountered when dealing with stance dynamics, identify open challenges and discuss future directions in three key dimensions: utterance, context and influence.

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Section: Stance Utterancementioning

confidence: 99%

Capturing stance dynamics in social media: open challenges and research directions

Alkhalifa

Zubiaga

2022

Int J Digit Humanities

View full text Add to dashboard Cite

show abstract

“…For example, in the utterance "I am supportive of A, but I'm totally against B", the author expresses a supporting stance towards target A and an opposing stance towards target B. The target may be implicit and not always directly mentioned in the text [77]. The target may be implicitly referred to [83], or only aspects of it may be mentioned [11].…”

Section: Stance Utterancementioning

confidence: 99%

Capturing Stance Dynamics in Social Media: Open Challenges and Research Directions

Alkhalifa¹,

Zubiaga²

2021

Preprint

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Social media platforms provide a goldmine for mining public opinion on issues of wide societal interest. Opinion mining is a problem that can be operationalised by capturing and aggregating the stance of individual social media posts as supporting, opposing or being neutral towards the issue at hand. While most prior work in stance detection has investigated datasets with limited time coverage, interest in investigating longitudinal datasets has recently increased. Evolving dynamics in linguistic and behavioural patterns observed in new data require in turn adapting stance detection systems to deal with the changes. In this survey paper, we investigate the intersection between computational linguistics and the temporal evolution of human communication in digital media. We perform a critical review in emerging research considering dynamics, exploring different semantic and pragmatic factors that impact linguistic data in general, and stance particularly. We further discuss current directions in capturing stance dynamics in social media. We organise the challenges of dealing with stance dynamics, identify open challenges and discuss future directions in three key dimensions: utterance, context and influence.

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Inspiring Heterogeneous Perspectives in News Media Comment Sections

Steimann

Feger

Mauve

2022

Human Interface and the Management of Information: Visual and Information Design

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Improving Implicit Stance Classification in Tweets Using Word and Sentence Embeddings

Cited by 5 publications

References 10 publications

Capturing stance dynamics in social media: open challenges and research directions

Capturing stance dynamics in social media: open challenges and research directions

Capturing Stance Dynamics in Social Media: Open Challenges and Research Directions

Inspiring Heterogeneous Perspectives in News Media Comment Sections

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