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    <abstract>Topic-specificity is often seen as a limitation of stance detection models and datasets, especially for analyzing political and societal debates. However, stances contain topic-specific aspects that are crucial for an in-depth understanding of these debates. Our interdisciplinary approach identifies social science theories on specific debate topics as an opportunity for further defining stance detection research and analyzing online debate. This paper explores sustainability as debate topic, and connects stance to the sustainability-related Value-Belief-Norm (VBN) theory. VBN theory states that arguments in favor or against sustainability initiatives contain the dimensions of feeling power to change the issue with the initiative, and thinking whether or not the initiative tackles an urgent threat to the environment. In a pilot study with our Reddit European Sustainability Initiatives corpus, we develop an annotation procedure for these complex concepts. We then compare crowd-workers with Natural Language Processing experts’ annotation proficiency. Both crowd-workers and NLP experts find the tasks difficult, but experts reach more agreement on some difficult examples. This pilot study shows that complex theories about debate topics are feasible and worthwhile as annotation tasks for stance detection.</abstract>
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%0 Conference Proceedings
%T Topic-specific social science theory in stance detection: a proposal and interdisciplinary pilot study on sustainability initiatives
%A Reuver, Myrthe
%A Polimeno, Alessandra
%A Fokkens, Antske
%A Lopes, Ana Isabel
%Y Klamm, Christopher
%Y Lapesa, Gabriella
%Y Ponzetto, Simone Paolo
%Y Rehbein, Ines
%Y Sen, Indira
%S Proceedings of the 4th Workshop on Computational Linguistics for the Political and Social Sciences: Long and short papers
%D 2024
%8 September
%I Association for Computational Linguistics
%C Vienna, Austria
%F reuver-etal-2024-topic
%X Topic-specificity is often seen as a limitation of stance detection models and datasets, especially for analyzing political and societal debates. However, stances contain topic-specific aspects that are crucial for an in-depth understanding of these debates. Our interdisciplinary approach identifies social science theories on specific debate topics as an opportunity for further defining stance detection research and analyzing online debate. This paper explores sustainability as debate topic, and connects stance to the sustainability-related Value-Belief-Norm (VBN) theory. VBN theory states that arguments in favor or against sustainability initiatives contain the dimensions of feeling power to change the issue with the initiative, and thinking whether or not the initiative tackles an urgent threat to the environment. In a pilot study with our Reddit European Sustainability Initiatives corpus, we develop an annotation procedure for these complex concepts. We then compare crowd-workers with Natural Language Processing experts’ annotation proficiency. Both crowd-workers and NLP experts find the tasks difficult, but experts reach more agreement on some difficult examples. This pilot study shows that complex theories about debate topics are feasible and worthwhile as annotation tasks for stance detection.
%U https://aclanthology.org/2024.cpss-1.8
%P 101-111

Markdown (Informal)

[Topic-specific social science theory in stance detection: a proposal and interdisciplinary pilot study on sustainability initiatives](https://aclanthology.org/2024.cpss-1.8) (Reuver et al., cpss-WS 2024)

• Topic-specific social science theory in stance detection: a proposal and interdisciplinary pilot study on sustainability initiatives (Reuver et al., cpss-WS 2024)

ACL

• Myrthe Reuver, Alessandra Polimeno, Antske Fokkens, and Ana Isabel Lopes. 2024. Topic-specific social science theory in stance detection: a proposal and interdisciplinary pilot study on sustainability initiatives. In Proceedings of the 4th Workshop on Computational Linguistics for the Political and Social Sciences: Long and short papers, pages 101–111, Vienna, Austria. Association for Computational Linguistics.