mlsanityData to 7 Oct 2026

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

arXiv 2109.00475 ↗First posted 1 Sep 2021cs.CLcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 37 of its 114 references that are in the map. First posted on arXiv 1 Sep 2021.

Builds on

37 of 114 references.

References · 37

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25 more
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Cited by year

Citing papers per year: 2022: 1; 2023: 1; 2024: 2.

4 papers.

  1. 2023Prompting and Fine-Tuning Open-Sourced Large Language Models for Stance Classificationcited by 16
  2. 2022Building for Tomorrow: Assessing the Temporal Persistence of Text Classifierscited by 8
  3. 2024SocialPET: Socially Informed Pattern Exploiting Training for Few-Shot Stance Detection in Social Mediacited by 1
  4. 2024Zero-shot Cross-lingual Stance Detection via Adversarial Language Adaptationcited by 1