Political Leaning and Politicalness Classification of Texts
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866913948690808832 |
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| author | Volf, Matous Simko, Jakub |
| author_facet | Volf, Matous Simko, Jakub |
| contents | This paper addresses the challenge of automatically classifying text according to political leaning and politicalness using transformer models. We compose a comprehensive overview of existing datasets and models for these tasks, finding that current approaches create siloed solutions that perform poorly on out-of-distribution texts. To address this limitation, we compile a diverse dataset by combining 12 datasets for political leaning classification and creating a new dataset for politicalness by extending 18 existing datasets with the appropriate label. Through extensive benchmarking with leave-one-in and leave-one-out methodologies, we evaluate the performance of existing models and train new ones with enhanced generalization capabilities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_13913 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Political Leaning and Politicalness Classification of Texts Volf, Matous Simko, Jakub Computation and Language Artificial Intelligence This paper addresses the challenge of automatically classifying text according to political leaning and politicalness using transformer models. We compose a comprehensive overview of existing datasets and models for these tasks, finding that current approaches create siloed solutions that perform poorly on out-of-distribution texts. To address this limitation, we compile a diverse dataset by combining 12 datasets for political leaning classification and creating a new dataset for politicalness by extending 18 existing datasets with the appropriate label. Through extensive benchmarking with leave-one-in and leave-one-out methodologies, we evaluate the performance of existing models and train new ones with enhanced generalization capabilities. |
| title | Political Leaning and Politicalness Classification of Texts |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2507.13913 |