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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/2505.20963 |
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| _version_ | 1866912397623558144 |
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| author | Krejca, Felix Kietreiber, Tobias Buchelt, Alexander Neumaier, Sebastian |
| author_facet | Krejca, Felix Kietreiber, Tobias Buchelt, Alexander Neumaier, Sebastian |
| contents | The increasing volume of online discussions requires advanced automatic content moderation to maintain responsible discourse. While hate speech detection on social media is well-studied, research on German-language newspaper forums remains limited. Existing studies often neglect platform-specific context, such as user history and article themes. This paper addresses this gap by developing and evaluating binary classification models for automatic content moderation in German newspaper forums, incorporating contextual information. Using LSTM, CNN, and ChatGPT-3.5 Turbo, and leveraging the One Million Posts Corpus from the Austrian newspaper Der Standard, we assess the impact of context-aware models. Results show that CNN and LSTM models benefit from contextual information and perform competitively with state-of-the-art approaches. In contrast, ChatGPT's zero-shot classification does not improve with added context and underperforms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_20963 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Context-Aware Content Moderation for German Newspaper Comments Krejca, Felix Kietreiber, Tobias Buchelt, Alexander Neumaier, Sebastian Computation and Language Artificial Intelligence The increasing volume of online discussions requires advanced automatic content moderation to maintain responsible discourse. While hate speech detection on social media is well-studied, research on German-language newspaper forums remains limited. Existing studies often neglect platform-specific context, such as user history and article themes. This paper addresses this gap by developing and evaluating binary classification models for automatic content moderation in German newspaper forums, incorporating contextual information. Using LSTM, CNN, and ChatGPT-3.5 Turbo, and leveraging the One Million Posts Corpus from the Austrian newspaper Der Standard, we assess the impact of context-aware models. Results show that CNN and LSTM models benefit from contextual information and perform competitively with state-of-the-art approaches. In contrast, ChatGPT's zero-shot classification does not improve with added context and underperforms. |
| title | Context-Aware Content Moderation for German Newspaper Comments |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2505.20963 |