@inproceedings{arampatzis-arampatzis-2026-duth-semeval-2026,
title = "{DUTH} at {S}em{E}val-2026 Task 9: Joint Multilingual Fine-Tuning for Online Polarization Detection",
author = "Arampatzis, Georgios and
Arampatzis, Avi",
editor = "Kochmar, Ekaterina and
Ghosh, Debanjan and
North, Kai and
Komachi, Mamoru",
booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.semeval-1.86/",
pages = "599--604",
ISBN = "979-8-89176-414-9",
abstract = "Online polarization on social media presentssubstantial challenges for public discourse, content moderation, and large-scale social analytics across diverse linguistic and cultural contexts. A recent multilingual benchmark enablessystematic evaluation of polarization detectionacross 22 languages and multiple sociopoliticalevents, providing a unified setting for studying socially grounded NLP under multilingualconditions.Wepresent DUTH, a unified multilingual system for binary polarization detection based onjoint fine-tuning of XLM-RoBERTa on the 22languages of SemEval-2026 Task 9 Subtask1. Our system uses a single shared encoderwith a linear classification head and is trainedjointly on the multilingual training set usingmixed-precision optimization. On the officialevaluation, the system achieved an average Accuracy of 0.822 and an average Macro-F1 of0.780 across 22 languages. The results showthat a simple jointly fine-tuned multilingualtransformer provides a competitive and scalable baseline for online polarization detection,while still facing difficulties in implicit, sarcastic, and culturally grounded cases."
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<abstract>Online polarization on social media presentssubstantial challenges for public discourse, content moderation, and large-scale social analytics across diverse linguistic and cultural contexts. A recent multilingual benchmark enablessystematic evaluation of polarization detectionacross 22 languages and multiple sociopoliticalevents, providing a unified setting for studying socially grounded NLP under multilingualconditions.Wepresent DUTH, a unified multilingual system for binary polarization detection based onjoint fine-tuning of XLM-RoBERTa on the 22languages of SemEval-2026 Task 9 Subtask1. Our system uses a single shared encoderwith a linear classification head and is trainedjointly on the multilingual training set usingmixed-precision optimization. On the officialevaluation, the system achieved an average Accuracy of 0.822 and an average Macro-F1 of0.780 across 22 languages. The results showthat a simple jointly fine-tuned multilingualtransformer provides a competitive and scalable baseline for online polarization detection,while still facing difficulties in implicit, sarcastic, and culturally grounded cases.</abstract>
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%0 Conference Proceedings
%T DUTH at SemEval-2026 Task 9: Joint Multilingual Fine-Tuning for Online Polarization Detection
%A Arampatzis, Georgios
%A Arampatzis, Avi
%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 arampatzis-arampatzis-2026-duth-semeval-2026
%X Online polarization on social media presentssubstantial challenges for public discourse, content moderation, and large-scale social analytics across diverse linguistic and cultural contexts. A recent multilingual benchmark enablessystematic evaluation of polarization detectionacross 22 languages and multiple sociopoliticalevents, providing a unified setting for studying socially grounded NLP under multilingualconditions.Wepresent DUTH, a unified multilingual system for binary polarization detection based onjoint fine-tuning of XLM-RoBERTa on the 22languages of SemEval-2026 Task 9 Subtask1. Our system uses a single shared encoderwith a linear classification head and is trainedjointly on the multilingual training set usingmixed-precision optimization. On the officialevaluation, the system achieved an average Accuracy of 0.822 and an average Macro-F1 of0.780 across 22 languages. The results showthat a simple jointly fine-tuned multilingualtransformer provides a competitive and scalable baseline for online polarization detection,while still facing difficulties in implicit, sarcastic, and culturally grounded cases.
%U https://aclanthology.org/2026.semeval-1.86/
%P 599-604
Markdown (Informal)
[DUTH at SemEval-2026 Task 9: Joint Multilingual Fine-Tuning for Online Polarization Detection](https://aclanthology.org/2026.semeval-1.86/) (Arampatzis & Arampatzis, SemEval 2026)
ACL