References

Bachl, M., & Scharkow, M. (2024). Computational text analysis. OSF. https://doi.org/10.31219/osf.io/3yhu8

Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? 🦜. Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–623. https://doi.org/gh677h

Chae, Y., & Davidson, T. (2025). Large language models for text classification: From zero-shot learning to instruction-tuning. Sociological Methods & Research. https://doi.org/g9pqfk

Gilardi, F., Alizadeh, M., & Kubli, M. (2023). ChatGPT outperforms crowd workers for text-annotation tasks. Proceedings of the National Academy of Sciences, 120(30), e2305016120. https://doi.org/gsqx5m

Heseltine, M., & Clemm von Hohenberg, B. (2024). Large language models as a substitute for human experts in annotating political text. Research & Politics, 11(1). https://doi.org/gtkhqr

Kathirgamalingam, A., Lind, F., Bernhard, J., & Boomgaarden, H. G. (2024). Agree to disagree? Human and LLM coder bias for constructs of marginalization. OSF. https://doi.org/10.31235/osf.io/agpyr

Krippendorff, K. (2019). Content analysis: An introduction to its methodology (4th ed.). SAGE Publications, Inc. https://doi.org/mmsp

Kroon, A., Welbers, K., Trilling, D., & Atteveldt, W. van. (2024). Advancing automated content analysis for a new era of media effects research: The key role of transfer learning. Communication Methods and Measures, 18(2), 142–162. https://doi.org/gsv44t

Neuendorf, K. A. (2017). The content analysis guidebook. SAGE Publications, Inc. https://doi.org/dz7p

Rathje, S., Mirea, D.-M., Sucholutsky, I., Marjieh, R., Robertson, C. E., & Van Bavel, J. J. (2024). GPT is an effective tool for multilingual psychological text analysis. Proceedings of the National Academy of Sciences, 121(34), e2308950121. https://doi.org/gt7hrw

Spirling, A. (2023). Why open-source generative AI models are an ethical way forward for science. Nature, 616(7957), 413–413. https://doi.org/gsqx6v

Stoll, A., Yu, J., Andrich, A., & Domahidi, E. (2025). Classification bias of LLMs in detecting incivility towards female and male politicians in German social media discourse. Communication Methods and Measures. https://doi.org/g94g68

Stolwijk, S. B., Boukes, M., Yeung, W. N., Liao, Y., Münker, S., Kroon, A. C., & Trilling, D. (2025). Can we use automated approaches to measure the quality of online political discussion? How to (not) measure interactivity, diversity, rationality, and incivility in online comments to the news. Communication Methods and Measures. https://doi.org/g93sqk

Stuhler, O., Ton, C. D., & Ollion, E. (2025). From codebooks to promptbooks: Extracting information from text with generative large language models. Sociological Methods & Research. https://doi.org/g9vgnq

Törnberg, P. (2024a). Best practices for text annotation with large language models. Sociologica, 18(2), 67–85. https://doi.org/g9vgm7

Törnberg, P. (2024b). Large language models outperform expert coders and supervised classifiers at annotating political social media messages. Social Science Computer Review. https://doi.org/g8nnfx

Van Atteveldt, W., Trilling, D., & Arcila Calderón, C. (2022). Computational analysis of communication. Wiley Blackwell. https://v2.cssbook.net/

Widder, D. G., Whittaker, M., & West, S. M. (2024). Why “open” AI systems are actually closed, and why this matters. Nature, 635(8040), 827–833. https://doi.org/g8xdb3