taz2024full: Analysing German Newspapers for Gender Bias and Discrimination across Decades
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915328727973888 |
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| author | Urchs, Stefanie Thurner, Veronika Aßenmacher, Matthias Heumann, Christian Thiemichen, Stephanie |
| author_facet | Urchs, Stefanie Thurner, Veronika Aßenmacher, Matthias Heumann, Christian Thiemichen, Stephanie |
| contents | Open-access corpora are essential for advancing natural language processing (NLP) and computational social science (CSS). However, large-scale resources for German remain limited, restricting research on linguistic trends and societal issues such as gender bias. We present taz2024full, the largest publicly available corpus of German newspaper articles to date, comprising over 1.8 million texts from taz, spanning 1980 to 2024.
As a demonstration of the corpus's utility for bias and discrimination research, we analyse gender representation across four decades of reporting. We find a consistent overrepresentation of men, but also a gradual shift toward more balanced coverage in recent years. Using a scalable, structured analysis pipeline, we provide a foundation for studying actor mentions, sentiment, and linguistic framing in German journalistic texts.
The corpus supports a wide range of applications, from diachronic language analysis to critical media studies, and is freely available to foster inclusive and reproducible research in German-language NLP. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_05388 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | taz2024full: Analysing German Newspapers for Gender Bias and Discrimination across Decades Urchs, Stefanie Thurner, Veronika Aßenmacher, Matthias Heumann, Christian Thiemichen, Stephanie Computation and Language Open-access corpora are essential for advancing natural language processing (NLP) and computational social science (CSS). However, large-scale resources for German remain limited, restricting research on linguistic trends and societal issues such as gender bias. We present taz2024full, the largest publicly available corpus of German newspaper articles to date, comprising over 1.8 million texts from taz, spanning 1980 to 2024. As a demonstration of the corpus's utility for bias and discrimination research, we analyse gender representation across four decades of reporting. We find a consistent overrepresentation of men, but also a gradual shift toward more balanced coverage in recent years. Using a scalable, structured analysis pipeline, we provide a foundation for studying actor mentions, sentiment, and linguistic framing in German journalistic texts. The corpus supports a wide range of applications, from diachronic language analysis to critical media studies, and is freely available to foster inclusive and reproducible research in German-language NLP. |
| title | taz2024full: Analysing German Newspapers for Gender Bias and Discrimination across Decades |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2506.05388 |