Published in APIs for social scientists: A collaborative review, 2023
Introduction to the Spotify API for social scientists.
Recommended citation: Hölzl, Johanna, Marie-Lou Sohnius. (2023). "Spotify API." In APIs for social scientists: A collaborative review, edited by P. C. Bauer, C. Landesvatter, and L. Behrens.
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Published in GESIS Data Archive, 2025
Data set on digital inequalities in Germany. The data for this project come from a web survey of people living in Germany with Internet access aged between 18 and 64. Respondents were recruited via the Bilendi online access panel. From August 7 to 16, 2024, 1,052 respondents completed the questionnaire in full. Simple quotas for age, education and gender were used to approximate the target population. The survey includes questions on attitudes towards artificial intelligence and the Large Language Model ChatGPT, digital competences, job satisfaction, job insecurity and digitalization in the workplace, the use of social media, political attitudes and radicalism, gender role attitudes, and socio-demographics.
Recommended citation: Hölzl, Johanna, Daria Szafran, Florian Keusch. (2025). "Digitale Ungleichheiten in Deutschland." GESIS Data Archive. DOI: 10.7802/2915
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Published in Social Science Research, 2025
Researchers increasingly use aggregated search data from Google Trends to study a wide range of phenomena. Although this new data source possesses some important practical and methodological benefits, it also carries substantial challenges with respect to internal validity, reliability, and generalizability. In this paper, we describe and assess the existing applied research with Google Trends data in the social sciences. We conduct a systematic literature review of 360 studies using Google Trends data to (1) illustrate habits and trends and (2) examine whether and how researchers take the identified challenges into account. The results show that the large majority of the literature fails to test the internal validity of their Google Trends measure, does not consider whether their data are reliable across samples, and does not discuss the generalizability of their results. We conclude by stating practical recommendations that will help researchers to address these issues and properly work with Google Trends data.
Recommended citation: Hölzl, Johanna, Florian Keusch, Christoph Sajons. (2025). "The (mis)use of Google Trends data in the social sciences - A systematic review, critique, and recommendations." Social Science Research. 126(103099). DOI: 10.1016/j.ssresearch.2024.103099
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Published in Preprint, 2026
The COVID-19 pandemic reignited longstanding debates around gender inequalities in paid and unpaid work. While survey research has advanced our understanding of these disparities, it typically relies on predefined categories and is susceptible to social desirability and recall bias. Online postings, by contrast, capture intimate experiences in real time but rarely include demographic attributes. We leverage a unique dataset of discussions in Reddit relationship communities that combines rich descriptions of conflicts around (un)paid work with demographic information to examine: Which topics do men and women discuss in relationship conflicts around paid and unpaid work? We use Large Language Models (LLMs) and systematically vary GPT-family models and prompting strategies to classify manifest (demographic information) and latent variables (whether posts discuss romantic relationships, paid, and unpaid work) in Reddit posts. Using classifications from the best-performing approach, we apply Structural Topic Models to explore which topics partners discuss and how discussions evolve over time. We find temporary pandemic shifts in topics, and women more often discuss mental health in paid work-related conflicts, while men more frequently emphasize career objectives. In conflicts related to unpaid work, men more frequently express concerns about sexual intimacy. Our analysis offers new insights into topics driving relationship conflicts around (un)paid work. We also contribute to the growing literature on LLMs’ capabilities and limitations in classifying social-science constructs. By combining demographic information with sensitive narratives, our dataset captures forms of relational conflict rarely accessible in survey or social-media research, opening promising avenues for future research.
Recommended citation: Zeyer Gliozzo, Birgit, Johanna Hölzl, Gundula Zoch, Philipp Doebler. (2026). ""My (22m) Girlfriend (23f) Comes Home and Does Nothing" — Gendered Perceptions of Paid and Household Labor in Reddit Relationship Discussions over Time." Preprint available on OSF: 10.17605/OSF.IO/F6D7E
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Published in Social Science Computer Review, 2026
To collect digital trace data, researchers continue to rely on for-profit companies’ Application Programming Interfaces (APIs). These APIs often return samples of the data based on intransparent sampling procedures and algorithms. In this paper, we extend research on the reliability of digital trace data from APIs by examining the effect of the download location on inconsistencies across returned samples: Do we get different values from digital trace data APIs depending on where we download the data from? We compare samples from Google Trends, YouTube Data, and the New York Times (NYT) API from four countries across three continents (Austria, Germany, the U.S., and Australia) for the same query parameters (i.e., search term, region, and time range). Our results show that the download location impacts the returned samples for all three APIs, depending on the query. We find large inconsistencies for samples from Google Trends and the YouTube Data API, while the NYT API returns identical article sets from each download location for most queries. We conclude with practical recommendations for researchers using these APIs. Our findings serve as a cautionary reminder for social scientists relying on sampling-based APIs as they point to yet another limitation regarding their reliability and reproducibility.
The study has been preregistered under 10.17605/OSF.IO/HDJ6Q for Google Trends and 10.17605/OSF.IO/U742B for the YouTube and NYT API.
Recommended citation: Hölzl, Johanna, Florian Keusch, John Collins. (2026). "Where You Are Is What You Get! Inconsistencies of Digital Trace Data across Download Locations." Social Science Computer Review 0(0). DOI: 10.1177/08944393261469494.
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Presenting author of joint work with Florian Keusch.
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Presenting work in progress from the project “Integration Research 2.0—Harnessing the Power of New Data Sources to Advance Knowledge on Behaviour and Attitudes of Migrants and Natives” (PIs: Florian Keusch and Christoph Sajons).
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Presenting author of joint work with Florian Keusch and Christoph Sajons.
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Presenting author of joint work with Florian Keusch and Christoph Sajons.
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Presenting author of joint work with Florian Keusch.
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Presenting author of joint work with Florian Keusch and John Collins.
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Presenting author of joint work with Florian Keusch and John Collins.
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Presenting author of joint work with Florian Keusch and John Collins.
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Presenting author of joint work with Florian Keusch and John Collins.
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Presenting author of joint work with Birgit Zeyer Gliozzo, Gundula Zoch, and Philipp Doebler.
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Presenting author of joint work with Florian Keusch, Christoph Sajons, and John Collins.
Bachelor's course, University of Mannheim, Chair of Social Data Science and Methodology, Spring and Fall 2023 and 2024
Two-semester, practice-oriented course on data collection and data analysis in R, guiding students through the entire research cycle:
Bachelor's course, University of Mannheim, Chair of Social Data Science and Methodology, Fall 2022, 2023, 2024, and 2025
Exercise course for first-semester Bachelor’s students in Sociology, accompanying the lecture Data Collection. The course introduces the fundamentals of empirical social research and guides students through the development of a first, hypothetical research project.
Bachelor's course, University of Mannheim, Chair of Social Data Science and Methodology, Spring 2024, 2025, and 2026
Colloquium accompanying the Bachelor’s thesis in Sociology, supporting students in the preparation and completion of a quantitative empirical thesis. The course provides close supervision throughout the research process, including guidance on topic development, research design, and the resolution of conceptual and statistical challenges. Students independently work on a diverse range of substantive topics of their choice and receive continuous feedback on their progress.