POLAR: A Benchmark for Multilingual, Multicultural, and Multi-Event Online Polarization
Usman Naseem, Robert Geislinger, Juan Ren, Sarah Kohail, Rudy Alexandro Garrido Veliz, P Sam Sahil, Yiran Zhang, Idris Abdulmumin, Marco Antonio Stranisci, Özge Alacam, Cengiz Acarturk, Aisha Jabr, Saba Anwar, Abinew Ali Ayele, Simona Frenda, Alessandra Teresa Cignarella, Elena Tutubalina, Oleg Rogov, Aung Kyaw Htet, Xintong Wang, Surendrabikram Thapa, Kritesh Rauniyar, Tanmoy Chakraborty, MD Arfeen Zeeshan, Dheeraj Kodati, Satya Keerthi, Sahar Moradizeyveh, Firoj Alam, Md Arid Hasan, Syed Ishtiaque Ahmed, Ye Kyaw Thu, Shantipriya Parida, Ihsan Ayyub Qazi, Lilian Diana Awuor Wanzare, Nelson Odhiambo Onyango, Clemencia Siro, Jane Wanjiru Kimani, Ibrahim Said Ahmad, Adem Chanie Ali, Martin Semmann, Chris Biemann, Shamsuddeen Hassan Muhammad, Seid Muhie Yimam
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Abstract
Online polarization poses a growing challenge for democratic discourse, yet most computational social science research remains monolingual, culturally narrow, or event-specific. We introduce POLAR, a multilingual, multicultural, and multi-event dataset with over 110K instances in 22 languages drawn from diverse online platforms and real-world events. Polarization is annotated along three axes, namely detection, type, and manifestation, using a variety of annotation platforms adapted to each cultural context. We conduct two main experiments: (1) fine-tuning six pretrained small language models; and (2) evaluating a range of open and closed large language models in few-shot and zero-shot settings. Results show that while most models perform well on binary polarization detection, they achieve substantially lower performance when predicting polarization types and manifestations. These findings highlight the complex, highly contextual nature of polarization and underscore the need for robust, adaptable approaches in NLP and computational social science. All resources will be released to support further research and effective mitigation of digital polarization globally.- Anthology ID:
- 2026.findings-acl.1433
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2026
- Month:
- July
- Year:
- 2026
- Address:
- San Diego, California, United States
- Editors:
- Maria Liakata, Viviane P. Moreira, Jiajun Zhang, David Jurgens
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 28699–28720
- Language:
- URL:
- https://aclanthology.org/2026.findings-acl.1433/
- DOI:
- Bibkey:
- Cite (ACL):
- Usman Naseem, Robert Geislinger, Juan Ren, Sarah Kohail, Rudy Alexandro Garrido Veliz, P Sam Sahil, Yiran Zhang, Idris Abdulmumin, Marco Antonio Stranisci, Özge Alacam, Cengiz Acarturk, Aisha Jabr, Saba Anwar, Abinew Ali Ayele, Simona Frenda, Alessandra Teresa Cignarella, Elena Tutubalina, Oleg Rogov, Aung Kyaw Htet, Xintong Wang, Surendrabikram Thapa, Kritesh Rauniyar, Tanmoy Chakraborty, MD Arfeen Zeeshan, Dheeraj Kodati, Satya Keerthi, Sahar Moradizeyveh, Firoj Alam, Md Arid Hasan, Syed Ishtiaque Ahmed, Ye Kyaw Thu, Shantipriya Parida, Ihsan Ayyub Qazi, Lilian Diana Awuor Wanzare, Nelson Odhiambo Onyango, Clemencia Siro, Jane Wanjiru Kimani, Ibrahim Said Ahmad, Adem Chanie Ali, Martin Semmann, Chris Biemann, Shamsuddeen Hassan Muhammad, and Seid Muhie Yimam. 2026. POLAR: A Benchmark for Multilingual, Multicultural, and Multi-Event Online Polarization. In Findings of the Association for Computational Linguistics: ACL 2026, pages 28699–28720, San Diego, California, United States. Association for Computational Linguistics.
- Cite (Informal):
- POLAR: A Benchmark for Multilingual, Multicultural, and Multi-Event Online Polarization (Naseem et al., Findings 2026)
- Copy Citation:
- PDF:
- https://aclanthology.org/2026.findings-acl.1433.pdf
- Checklist:
- 2026.findings-acl.1433.checklist.pdf
Export citation
@inproceedings{naseem-etal-2026-polar,
title = "{POLAR}: A Benchmark for Multilingual, Multicultural, and Multi-Event Online Polarization",
author = {Naseem, Usman and
Geislinger, Robert and
Ren, Juan and
Kohail, Sarah and
Veliz, Rudy Alexandro Garrido and
Sahil, P Sam and
Zhang, Yiran and
Abdulmumin, Idris and
Stranisci, Marco Antonio and
Alacam, {\"O}zge and
Acarturk, Cengiz and
Jabr, Aisha and
Anwar, Saba and
Ayele, Abinew Ali and
Frenda, Simona and
Cignarella, Alessandra Teresa and
Tutubalina, Elena and
Rogov, Oleg and
Htet, Aung Kyaw and
Wang, Xintong and
Thapa, Surendrabikram and
Rauniyar, Kritesh and
Chakraborty, Tanmoy and
Zeeshan, MD Arfeen and
Kodati, Dheeraj and
Keerthi, Satya and
Moradizeyveh, Sahar and
Alam, Firoj and
Hasan, Md Arid and
Ahmed, Syed Ishtiaque and
Thu, Ye Kyaw and
Parida, Shantipriya and
Qazi, Ihsan Ayyub and
Wanzare, Lilian Diana Awuor and
Onyango, Nelson Odhiambo and
Siro, Clemencia and
Kimani, Jane Wanjiru and
Ahmad, Ibrahim Said and
Ali, Adem Chanie and
Semmann, Martin and
Biemann, Chris and
Muhammad, Shamsuddeen Hassan and
Yimam, Seid Muhie},
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1433/",
pages = "28699--28720",
ISBN = "979-8-89176-395-1",
abstract = "Online polarization poses a growing challenge for democratic discourse, yet most computational social science research remains monolingual, culturally narrow, or event-specific. We introduce POLAR, a multilingual, multicultural, and multi-event dataset with over 110K instances in 22 languages drawn from diverse online platforms and real-world events. Polarization is annotated along three axes, namely detection, type, and manifestation, using a variety of annotation platforms adapted to each cultural context. We conduct two main experiments: (1) fine-tuning six pretrained small language models; and (2) evaluating a range of open and closed large language models in few-shot and zero-shot settings. Results show that while most models perform well on binary polarization detection, they achieve substantially lower performance when predicting polarization types and manifestations. These findings highlight the complex, highly contextual nature of polarization and underscore the need for robust, adaptable approaches in NLP and computational social science. All resources will be released to support further research and effective mitigation of digital polarization globally."
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<abstract>Online polarization poses a growing challenge for democratic discourse, yet most computational social science research remains monolingual, culturally narrow, or event-specific. We introduce POLAR, a multilingual, multicultural, and multi-event dataset with over 110K instances in 22 languages drawn from diverse online platforms and real-world events. Polarization is annotated along three axes, namely detection, type, and manifestation, using a variety of annotation platforms adapted to each cultural context. We conduct two main experiments: (1) fine-tuning six pretrained small language models; and (2) evaluating a range of open and closed large language models in few-shot and zero-shot settings. Results show that while most models perform well on binary polarization detection, they achieve substantially lower performance when predicting polarization types and manifestations. These findings highlight the complex, highly contextual nature of polarization and underscore the need for robust, adaptable approaches in NLP and computational social science. All resources will be released to support further research and effective mitigation of digital polarization globally.</abstract>
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%0 Conference Proceedings %T POLAR: A Benchmark for Multilingual, Multicultural, and Multi-Event Online Polarization %A Naseem, Usman %A Geislinger, Robert %A Ren, Juan %A Kohail, Sarah %A Veliz, Rudy Alexandro Garrido %A Sahil, P. Sam %A Zhang, Yiran %A Abdulmumin, Idris %A Stranisci, Marco Antonio %A Alacam, Özge %A Acarturk, Cengiz %A Jabr, Aisha %A Anwar, Saba %A Ayele, Abinew Ali %A Frenda, Simona %A Cignarella, Alessandra Teresa %A Tutubalina, Elena %A Rogov, Oleg %A Htet, Aung Kyaw %A Wang, Xintong %A Thapa, Surendrabikram %A Rauniyar, Kritesh %A Chakraborty, Tanmoy %A Zeeshan, MD Arfeen %A Kodati, Dheeraj %A Keerthi, Satya %A Moradizeyveh, Sahar %A Alam, Firoj %A Hasan, Md Arid %A Ahmed, Syed Ishtiaque %A Thu, Ye Kyaw %A Parida, Shantipriya %A Qazi, Ihsan Ayyub %A Wanzare, Lilian Diana Awuor %A Onyango, Nelson Odhiambo %A Siro, Clemencia %A Kimani, Jane Wanjiru %A Ahmad, Ibrahim Said %A Ali, Adem Chanie %A Semmann, Martin %A Biemann, Chris %A Muhammad, Shamsuddeen Hassan %A Yimam, Seid Muhie %Y Liakata, Maria %Y Moreira, Viviane P. %Y Zhang, Jiajun %Y Jurgens, David %S Findings of the Association for Computational Linguistics: ACL 2026 %D 2026 %8 July %I Association for Computational Linguistics %C San Diego, California, United States %@ 979-8-89176-395-1 %F naseem-etal-2026-polar %X Online polarization poses a growing challenge for democratic discourse, yet most computational social science research remains monolingual, culturally narrow, or event-specific. We introduce POLAR, a multilingual, multicultural, and multi-event dataset with over 110K instances in 22 languages drawn from diverse online platforms and real-world events. Polarization is annotated along three axes, namely detection, type, and manifestation, using a variety of annotation platforms adapted to each cultural context. We conduct two main experiments: (1) fine-tuning six pretrained small language models; and (2) evaluating a range of open and closed large language models in few-shot and zero-shot settings. Results show that while most models perform well on binary polarization detection, they achieve substantially lower performance when predicting polarization types and manifestations. These findings highlight the complex, highly contextual nature of polarization and underscore the need for robust, adaptable approaches in NLP and computational social science. All resources will be released to support further research and effective mitigation of digital polarization globally. %U https://aclanthology.org/2026.findings-acl.1433/ %P 28699-28720
Markdown (Informal)
[POLAR: A Benchmark for Multilingual, Multicultural, and Multi-Event Online Polarization](https://aclanthology.org/2026.findings-acl.1433/) (Naseem et al., Findings 2026)
- POLAR: A Benchmark for Multilingual, Multicultural, and Multi-Event Online Polarization (Naseem et al., Findings 2026)
ACL
- Usman Naseem, Robert Geislinger, Juan Ren, Sarah Kohail, Rudy Alexandro Garrido Veliz, P Sam Sahil, Yiran Zhang, Idris Abdulmumin, Marco Antonio Stranisci, Özge Alacam, Cengiz Acarturk, Aisha Jabr, Saba Anwar, Abinew Ali Ayele, Simona Frenda, Alessandra Teresa Cignarella, Elena Tutubalina, Oleg Rogov, Aung Kyaw Htet, Xintong Wang, Surendrabikram Thapa, Kritesh Rauniyar, Tanmoy Chakraborty, MD Arfeen Zeeshan, Dheeraj Kodati, Satya Keerthi, Sahar Moradizeyveh, Firoj Alam, Md Arid Hasan, Syed Ishtiaque Ahmed, Ye Kyaw Thu, Shantipriya Parida, Ihsan Ayyub Qazi, Lilian Diana Awuor Wanzare, Nelson Odhiambo Onyango, Clemencia Siro, Jane Wanjiru Kimani, Ibrahim Said Ahmad, Adem Chanie Ali, Martin Semmann, Chris Biemann, Shamsuddeen Hassan Muhammad, and Seid Muhie Yimam. 2026. POLAR: A Benchmark for Multilingual, Multicultural, and Multi-Event Online Polarization. In Findings of the Association for Computational Linguistics: ACL 2026, pages 28699–28720, San Diego, California, United States. Association for Computational Linguistics.