taz2024full: Analysing G erman Newspapers for Gender Bias and Discrimination across Decades (2025)

Abstract: 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. Wepresent taz2024full, the largest publicly available corpus of German newspaper articles to date, comprising over 1.8 million texts fromtaz, spanning 1980 to 2024.As a demonstration of the corpus’s utility for bias and discrimination research, we analyse gender representation across four decades ofreporting. We find a consistent overrepresentation of men, but also a gradual shift toward more balanced coverage in recent years. Usinga scalable, structured analysis pipeline, we provide a foundation for studying actor mentions, sentiment, and linguistic framing in Germanjournalistic texts.The corpus supports a wide range of applications, from diachronic language analysis to critical media studies, and is freely available tofoster inclusive and reproducible research in German-language NLP.

## introduction understanding how language reflects and shapes society requires access to large, well-structured text collections. while english dominates the landscape of publicly available corpora, resources for other languages-such as german-remain comparatively scarce. this limits the scope of languagespecific research on topics like linguistic change, media discourse, and social bias. in particular, studying gender discrimination in news media calls for datasets that span long time periods and include sufficient volume and variation to analyse these phenomena at scale. given that the term "gender" is not always used uniformly, we define its usage in the scope of this paper: we refer to "gender" as a social construct, which is non-binary, flexible, shaped by individuals and by those perceiving them, rather than a biological given @xcite . however, due to methodological and linguistic constraints, we are limited to working within a binary gender spectrum in this work. we use pronouns to assume the gender of actors. as we are working on german data, we are limited to the commonly used pronouns in this language. since german lacks widely used non-binary pronouns, non-binary genders could not be included in our analysis. we further employ the definition of "biases" as "all notions and beliefs a person has towards another person or group of persons" @xcite . these biases can manifest in (written) discrimination, wherein a person or group is intentionally mistreated based on specific characteristics @xcite . contribution: in this work, we present a twofold contribution to the research area of gender bias and gender discrimination in written language and potentially also beyond: ## related work most publicly available news corpora focus on english newspapers, providing this type of diverse natural language processing (nlp) research resource only for a single language. the "chronicling america" dataset, provided by the library of congress, offers access to historical newspapers and digitised pages (spanning from 1690 to the present day) (library of congress). the "bbc news summary dataset" includes 2,225 documents from 2004 to 2005, covering five topical areas @xcite . the "news category dataset" foot_0 contains around 210,000 headlines and descriptions from https://www.huffpost.com , spanning 2012 to 2022 @xcite . the "news articles dataset" comprises articles from 2015 to 2017, scraped from https://www.thenews.com. pk/ , focusing on business and sports @xcite . larger corpora such as realnews, constructed from commoncrawl dumps, offer 120gb of deduplicated news articles from 2016 to 2019, targeting large-scale training and evaluation of llms @xcite . additionally, the "20 newsgroups" dataset foot_1 , widely used in nlp, contains approximately 20,000 documents grouped into 20 categories, though it is neither based on newspaper content nor does it specifically look at developments over time @xcite . regarding news datasets specifically for the german language, the "one million posts corpus" is one of the most prominent examples. it is derived from online discussions on the austrian newspaper der standard's website. it contains user posts from 2015-06-01 to 2016-05-31, with 11,773 labelled and 1,000,000 unlabelled entries, providing valuable insights into usergenerated content @xcite . this is, however, notably different from our resource as we do not focus on user-generated content, but on the articles themselves. in addition to this, several linguistic newspaper corpora exist, offering access to german news data. these include dwds (berlin-brandenburgischen akademie der wissenschaften), the tüpp-d/z corpus (seminar für sprachwissenschaft), the mannheim german reference corpus (dereko) @xcite , leipzig wortschatz (universität leipzig et al.), tiger @xcite , and others @xcite . however, these resources are often limited to keyword searches and return only sentence-level results, with most of them being unavailable for public use. these datasets highlight the diversity of resources available for english and german news research but also reveal limitations, such as restricted access and narrow use cases. this underscores the need for openly available, comprehensive datasets like taz2024full to support language-specific studies and address gaps in research on german news media. the detection of bias and discrimination in nlp has received significant attention in recent years. blodgett et al. (2020), sun et al. (2019), and shah et al. (2020) provide broad overviews of existing approaches, highlighting the diverse methodologies employed to identify, mitigate, and evaluate biases in textual data. these works discuss various strategies, from detecting stereotypical associations in embeddings to evaluating fairness in predictive systems. their surveys cover theoretical frameworks and practical implementations, offering valuable insights into the state of the field. despite the breadth of these reviews, none of the techniques discussed are directly comparable to the approach proposed by @xcite , which we adopt for evaluating our dataset. unlike many conventional bias detection methods, which focus on linguistic patterns, embeddings, or statistical measures, the method of @xcite combines information extraction and linguistic discourse analysis to identify markers of bias and discrimination at the actor level. this actor-focused approach enables a more nuanced examination of how individuals and groups are represented in text, making it particularly suited for analysing bias in news media. ## dataset we introduce the taz2024full newspaper corpus, a german newspaper corpus containing 1,834,370 publicly available articles published between 1980 and 2024 in the german newspaper "taz". this extensive dataset provides a valuable resource for linguistic, cultural, and societal research. it enables analyses across more than four decades of journalistic content. to the best of our knowledge, this is a unique collection of articles from a single news source over such an extended period, offering insights beyond specific use cases and opening avenues for long-term, diachronic studies (cf. section 4). the dataset covers a wide range of historical contexts, including events of global significance, such as 9/11, the financial crisis in europe, and the covid-19 pandemic, all of which have shaped discourse on an international scale. additionally, it encompasses events with particular relevance to germany, such as the reunion, the 2015 migrant crisis, and several political changes, providing researchers with a lens to explore national and regional impacts on public discourse. the corpus's temporal span allows for the analysis of how language, societal attitudes, and journalistic practices have evolved in response to these events. for our corpus, we exclusively used "taz" as a data source because no other german newspaper granted us free access to their archives. licensing fees would have been required, and even then, access to the full dataset would not have been guaranteed. furthermore, publishing the corpus would have been prohibited due to legal restrictions on data redistribution. we have chosen not to provide a predefined traintest split for the dataset. data splitting strategies may vary significantly depending on research objectives and use cases. for instance, studies focusing on the impact of specific historical events may require custom temporal splits, while others analysing long-term trends might need broader, cross-temporal divisions. allowing users to define their splits ensures maximum flexibility and adaptability for diverse research applications. by offering a corpus that captures the breadth and depth of "taz" reporting across decades, we aim to provide a foundation for a wide range of studies, from exploring shifts in public discourse to examining linguistic phenomena and identifying patterns of bias and discrimination. this adaptability makes the taz2024full corpus a critical resource for researchers in nlp, computational social science (css), and related fields. to our knowledge, this corpus is the largest german newspaper corpus available. other publicly accessible german newspaper corpora do not provide full access to their data; instead, they typically allow only keyword-based searches or sentencelevel queries through linguistic databases, making large-scale analysis impossible. ## experiments to showcase the utility of the taz2024full corpus, we conduct an illustrative analysis of gender representation in german newspaper articles over the past four decades. this analysis is not intended as an exhaustive study of bias, but rather as a demonstration of how the dataset can support research into long-term patterns of representation and framing. we build upon the pipeline introduced by urchs et al. (2024), which detects bias and discrimination at the actor level. while their work focused on english-language news, we adapt and scale an extended version of their pipeline to our german corpus, applying it to examine discrimination markers in journalistic reporting from 1980 to 2024. the full code is published on github foot_3 . see also appendix a for a sample report of 2023. ## conclusion and future work in this work, we introduced taz2024full, a comprehensive german newspaper corpus spanning over four decades. this dataset represents a significant resource for linguistic, cultural, and societal research, particularly in the areas of gender bias and discrimination in media. by leveraging a structured approach to analysing gender representation through actor mentions and predications, we provided insights into how "taz" has reported on women and men over time. our findings highlight a persistent imbalance in gender representation, with men not only appearing more frequently as actors but also receiving greater textual space. furthermore, we demonstrated the adaptability of an existing bias detection pipeline, originally designed for english texts, to large-scale germanlanguage data. our extensions included modifications tailored to the german linguistic landscape, such as pronoun-based gender identification and an analysis of the generic masculine. these enhancements offer a more refined approach to studying gender-related language use in german news media. looking ahead, several avenues for future research emerge. one priority is updating the corpus with new data beyond 2024 to enable ongoing diachronic analysis. additionally, incorporating topic modelling could provide deeper insights into the contextual framing of gender representation. since the current implementation would model topics at the sentence level, a necessary improvement would involve incorporating broader textual context to enhance topic coherence around actor predications. another promising direction is argument mining, which could refine our understanding of the implicit and explicit biases embedded in journalistic discourse. by identifying argument structures and rhetorical strategies, we could further uncover how gender bias manifests in media narratives. ultimately, we hope that taz2024full serves as a valuable resource for researchers in nlp, computational social science, and related fields, facilitating future studies on bias, representation, and media discourse in the german language. the taz2024full corpus and its languageagnostic pipeline provide a foundation for analysing bias and discrimination in german news media. future work will focus on expanding the corpus with newer data to ensure it remains relevant and reflective of contemporary discourse. enhancements to the pipeline include training sentiment analysis models tailored to german newspapers, improving pronoun coreference resolution for german texts, and incorporating argument mining for discrimination detection. additionally, we aim to extend the pipeline to support multimodal analysis by integrating text with non-textual data such as images and videos, enabling a comprehensive understanding of media content.