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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.20624 |
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| _version_ | 1866917250066284544 |
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| author | Naseem, Usman Geislinger, Robert Ren, Juan Kohail, Sarah Veliz, Rudy Garrido Sahil, P Sam Zhang, Yiran Stranisci, Marco Antonio Abdulmumin, Idris Alacam, Özge Acartürk, Cengiz Jabr, Aisha Anwar, Saba Ayele, Abinew Ali Frenda, Simona Cignarella, Alessandra Teresa Tutubalina, Elena Rogov, Oleg Htet, Aung Kyaw Wang, Xintong Thapa, Surendrabikram Rauniyar, Kritesh Chakraborty, Tanmoy Zeeshan, Arfeen Kodati, Dheeraj Keerthi, Satya Moradizeyveh, Sahar Alam, Firoj Hasan, Arid Ahmed, Syed Ishtiaque Thu, Ye Kyaw Parida, Shantipriya Qazi, Ihsan Ayyub Wanzare, Lilian Onyango, Nelson Odhiambo Siro, Clemencia Kimani, Jane Wanjiru Ahmad, Ibrahim Said Ali, Adem Chanie Semmann, Martin Biemann, Chris Muhammad, Shamsuddeen Hassan Yimam, Seid Muhie |
| author_facet | Naseem, Usman Geislinger, Robert Ren, Juan Kohail, Sarah Veliz, Rudy Garrido Sahil, P Sam Zhang, Yiran Stranisci, Marco Antonio Abdulmumin, Idris Alacam, Özge Acartürk, Cengiz Jabr, Aisha Anwar, Saba Ayele, Abinew Ali Frenda, Simona Cignarella, Alessandra Teresa Tutubalina, Elena Rogov, Oleg Htet, Aung Kyaw Wang, Xintong Thapa, Surendrabikram Rauniyar, Kritesh Chakraborty, Tanmoy Zeeshan, Arfeen Kodati, Dheeraj Keerthi, Satya Moradizeyveh, Sahar Alam, Firoj Hasan, Arid Ahmed, Syed Ishtiaque Thu, Ye Kyaw Parida, Shantipriya Qazi, Ihsan Ayyub Wanzare, Lilian Onyango, Nelson Odhiambo Siro, Clemencia Kimani, Jane Wanjiru Ahmad, Ibrahim Said Ali, Adem Chanie Semmann, Martin Biemann, Chris Muhammad, Shamsuddeen Hassan Yimam, Seid Muhie |
| contents | 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. The results show that, while most models perform well in 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 demonstrate 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_20624 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | POLAR: A Benchmark for Multilingual, Multicultural, and Multi-Event Online Polarization Naseem, Usman Geislinger, Robert Ren, Juan Kohail, Sarah Veliz, Rudy Garrido Sahil, P Sam Zhang, Yiran Stranisci, Marco Antonio Abdulmumin, Idris Alacam, Özge Acartürk, Cengiz Jabr, Aisha Anwar, Saba Ayele, Abinew Ali Frenda, Simona Cignarella, Alessandra Teresa Tutubalina, Elena Rogov, Oleg Htet, Aung Kyaw Wang, Xintong Thapa, Surendrabikram Rauniyar, Kritesh Chakraborty, Tanmoy Zeeshan, Arfeen Kodati, Dheeraj Keerthi, Satya Moradizeyveh, Sahar Alam, Firoj Hasan, Arid Ahmed, Syed Ishtiaque Thu, Ye Kyaw Parida, Shantipriya Qazi, Ihsan Ayyub Wanzare, Lilian Onyango, Nelson Odhiambo Siro, Clemencia Kimani, Jane Wanjiru Ahmad, Ibrahim Said Ali, Adem Chanie Semmann, Martin Biemann, Chris Muhammad, Shamsuddeen Hassan Yimam, Seid Muhie Computation and Language 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. The results show that, while most models perform well in 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 demonstrate 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. |
| title | POLAR: A Benchmark for Multilingual, Multicultural, and Multi-Event Online Polarization |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2505.20624 |