@inproceedings{daniyal-etal-2026-msqrd,
title = "{MS}qrd at {S}em{E}val-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization",
author = "Daniyal, Syeda Samah and
Badar, Muneeba and
Hasan, Manal and
Shah, Shifa and
Kumar, Sandesh and
Samad, Abdul",
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-1.214/",
pages = "1664--1671",
ISBN = "979-8-89176-414-9",
abstract = "Online polarization, the critical division between social, political, or identity groups, often leads to hate speech and social fragmentation. Detecting polarization, especially across diverse linguistic and cultural contexts, is a critical challenge. This paper presents our submission for SemEval-2026 Task 9, which focuses on detecting online polarization of multilingual, multicultural, and multievent (Naseem et al., 2025). The task is divided into three subtasks: (1) binary polarization detection, (2) multi-label classification of polarization type (e.g., political, racial, religious), and (3) multilabel identification of its manifestation (e.g., stereotype, vilification, dehumanization). For each subtask, we employ fine tune BERT-based transformer models. Model configurations are described in Section 4. The results are evaluated using the F1 macro score. We have achieved scores of 78.6, 55.8, 44.6 on the developmenttest set for subtasks 1, 2, and 3, respectively. Overall, the results demonstrate the effectiveness of BERT-based models for multilingual polarization detection."
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<abstract>Online polarization, the critical division between social, political, or identity groups, often leads to hate speech and social fragmentation. Detecting polarization, especially across diverse linguistic and cultural contexts, is a critical challenge. This paper presents our submission for SemEval-2026 Task 9, which focuses on detecting online polarization of multilingual, multicultural, and multievent (Naseem et al., 2025). The task is divided into three subtasks: (1) binary polarization detection, (2) multi-label classification of polarization type (e.g., political, racial, religious), and (3) multilabel identification of its manifestation (e.g., stereotype, vilification, dehumanization). For each subtask, we employ fine tune BERT-based transformer models. Model configurations are described in Section 4. The results are evaluated using the F1 macro score. We have achieved scores of 78.6, 55.8, 44.6 on the developmenttest set for subtasks 1, 2, and 3, respectively. Overall, the results demonstrate the effectiveness of BERT-based models for multilingual polarization detection.</abstract>
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%0 Conference Proceedings
%T MSqrd at SemEval-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization
%A Daniyal, Syeda Samah
%A Badar, Muneeba
%A Hasan, Manal
%A Shah, Shifa
%A Kumar, Sandesh
%A Samad, Abdul
%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 daniyal-etal-2026-msqrd
%X Online polarization, the critical division between social, political, or identity groups, often leads to hate speech and social fragmentation. Detecting polarization, especially across diverse linguistic and cultural contexts, is a critical challenge. This paper presents our submission for SemEval-2026 Task 9, which focuses on detecting online polarization of multilingual, multicultural, and multievent (Naseem et al., 2025). The task is divided into three subtasks: (1) binary polarization detection, (2) multi-label classification of polarization type (e.g., political, racial, religious), and (3) multilabel identification of its manifestation (e.g., stereotype, vilification, dehumanization). For each subtask, we employ fine tune BERT-based transformer models. Model configurations are described in Section 4. The results are evaluated using the F1 macro score. We have achieved scores of 78.6, 55.8, 44.6 on the developmenttest set for subtasks 1, 2, and 3, respectively. Overall, the results demonstrate the effectiveness of BERT-based models for multilingual polarization detection.
%U https://aclanthology.org/2026.semeval-1.214/
%P 1664-1671
Markdown (Informal)
[MSqrd at SemEval-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization](https://aclanthology.org/2026.semeval-1.214/) (Daniyal et al., SemEval 2026)
ACL
- Syeda Samah Daniyal, Muneeba Badar, Manal Hasan, Shifa Shah, Sandesh Kumar, and Abdul Samad. 2026. MSqrd at SemEval-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization. In Proceedings of the 20th International Workshop on Semantic Evaluation (2026), pages 1664–1671, San Diego, California, USA. Association for Computational Linguistics.