Experiments in News Bias Detection with Pre-Trained Neural Transformers
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866908751488876544 |
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| author | Menzner, Tim Leidner, Jochen L. |
| author_facet | Menzner, Tim Leidner, Jochen L. |
| contents | The World Wide Web provides unrivalled access to information globally, including factual news reporting and commentary. However, state actors and commercial players increasingly spread biased (distorted) or fake (non-factual) information to promote their agendas. We compare several large, pre-trained language models on the task of sentence-level news bias detection and sub-type classification, providing quantitative and qualitative results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_09938 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Experiments in News Bias Detection with Pre-Trained Neural Transformers Menzner, Tim Leidner, Jochen L. Computation and Language Artificial Intelligence The World Wide Web provides unrivalled access to information globally, including factual news reporting and commentary. However, state actors and commercial players increasingly spread biased (distorted) or fake (non-factual) information to promote their agendas. We compare several large, pre-trained language models on the task of sentence-level news bias detection and sub-type classification, providing quantitative and qualitative results. |
| title | Experiments in News Bias Detection with Pre-Trained Neural Transformers |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2406.09938 |