POLAR: A Benchmark for Multilingual, Multicultural, and Multi-Event Online Polarization
This addresses the need for more robust and adaptable approaches in NLP and computational social science to mitigate digital polarization globally, though it is incremental as it builds on existing datasets and methods.
The paper tackles the problem of online polarization by introducing POLAR, a multilingual, multicultural, and multievent dataset with over 23k instances in seven languages, and finds that while models perform well on binary polarization detection, they achieve substantially lower scores when predicting polarization types and manifestations.
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 multievent dataset with over 23k instances in seven languages from diverse online platforms and real-world events. Polarization is annotated along three axes: presence, type, and manifestation, using a variety of annotation platforms adapted to each cultural context. We conduct two main experiments: (1) we fine-tune six multilingual pretrained language models in both monolingual and cross-lingual setups; and (2) we evaluate a range of open and closed large language models (LLMs) in few-shot and zero-shot scenarios. Results show that while most models perform well on binary polarization detection, they achieve substantially lower scores when predicting polarization types and manifestations. These findings highlight the complex, highly contextual nature of polarization and 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.