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        <title>The Counterfactuals at SemEval-2026 Task 9: Can Counterfactually-Inspired Preprocessing help Detect Polarization?</title>
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            <namePart type="given">Ekaterina</namePart>
            <namePart type="family">Kochmar</namePart>
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            <namePart type="given">Debanjan</namePart>
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            <namePart type="given">Kai</namePart>
            <namePart type="family">North</namePart>
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            <namePart type="given">Mamoru</namePart>
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    <abstract>This paper presents the English-language submissions of The Counterfactuals team for the three subtasks of Task 9 at SemEval 2026. The task aims to detect multicultural online polarization, how it is expressed, and in what contexts. The task provides a high-quality annotation dataset of posts that follows a three-level schema: polarized or not (subtask 1), polarization type classification (subtask 2), and manifestation identification (subtask 3). I construct a pointwise mutual information-based lexicon that identifies highly-correlated words with the polarized class as labeled in subtask 1. Using this lexicon, I implement a large language model data augmentation technique. I then use the preprocessed datasets to finetune a BERT model (BERTweet) for each subtask. My highest performing models placed 48th out of 60, 35th out of 36, and 17th out of 24 on subtasks 1, 2, and 3 respectively. All code is available on GitHub.</abstract>
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%0 Conference Proceedings
%T The Counterfactuals at SemEval-2026 Task 9: Can Counterfactually-Inspired Preprocessing help Detect Polarization?
%A Johnson, Teagan
%Y Kochmar, Ekaterina
%Y Ghosh, Debanjan
%Y North, Kai
%Y Komachi, Mamoru
%S Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-414-9
%F johnson-2026-counterfactuals
%X This paper presents the English-language submissions of The Counterfactuals team for the three subtasks of Task 9 at SemEval 2026. The task aims to detect multicultural online polarization, how it is expressed, and in what contexts. The task provides a high-quality annotation dataset of posts that follows a three-level schema: polarized or not (subtask 1), polarization type classification (subtask 2), and manifestation identification (subtask 3). I construct a pointwise mutual information-based lexicon that identifies highly-correlated words with the polarized class as labeled in subtask 1. Using this lexicon, I implement a large language model data augmentation technique. I then use the preprocessed datasets to finetune a BERT model (BERTweet) for each subtask. My highest performing models placed 48th out of 60, 35th out of 36, and 17th out of 24 on subtasks 1, 2, and 3 respectively. All code is available on GitHub.
%U https://aclanthology.org/2026.semeval-1.57/
%P 387-401

Markdown (Informal)

[The Counterfactuals at SemEval-2026 Task 9: Can Counterfactually-Inspired Preprocessing help Detect Polarization?](https://aclanthology.org/2026.semeval-1.57/) (Johnson, SemEval 2026)

• The Counterfactuals at SemEval-2026 Task 9: Can Counterfactually-Inspired Preprocessing help Detect Polarization? (Johnson, SemEval 2026)

ACL

• Teagan Johnson. 2026. The Counterfactuals at SemEval-2026 Task 9: Can Counterfactually-Inspired Preprocessing help Detect Polarization?. In Proceedings of the 20th International Workshop on Semantic Evaluation (2026), pages 387–401, San Diego, California, USA. Association for Computational Linguistics.