The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
The Effects of Assumed AI vs. Human Authorship on the Perception of a GPT-Generated Text
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Abstract
Artificial Intelligence (AI) has demonstrated its ability to undertake writing tasks, including automated journalism. Prior studies suggest no differences between human and AI authors regarding perceived message credibility. However, research on people’s perceptions of AI authorship on complex topics is lacking. In a between-groups experiment (N = 734), we examined the effect of labeled authorship on credibility perceptions of a GPT-written science journalism article. The results of an equivalence test showed that labeling a text as AI-written vs. human-written reduced perceived message credibility (d = 0.36). Moreover, AI authorship decreased perceived source credibility (d = 0.24), anthropomorphism (d = 0.67), and intelligence (d = 0.41). The findings are discussed against the backdrop of a growing availability of AI-generated content and a greater awareness of AI authorship.
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“Prior work shows that content labeled as machine-produced is often viewed more skeptically than identical human-attributed text.”
From Authorship Attribution and Trust in AI-Generated Scientific Texts: An Experimental Study (2026) · cites this paper as Lermann Henestrosa & Kimmerle, 2024
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“readers tend to rate human-written items as higher quality than machine-written ones (Lermann Henestrosa & Kimmerle, 2024).”
From Authorship Attribution and Trust in AI-Generated Scientific Texts: An Experimental Study (2026) · cites this paper as Lermann Henestrosa & Kimmerle, 2024
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Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372
Equivalence Testing for Psychological Research: A Tutorial
Lakens, D., Scheel, A. M., & Isager, P. M. (2018) · Advances in Methods and Practices in Psychological Science · doi:10.1177/2515245918770963 · Cited by 1,560 · cited 1×
“For a detailed description of equivalence testing, see Lakens et al. (2018).”
Köbis, N., & Mossink, L. D. (2021) · Computers in Human Behavior · doi:10.1016/j.chb.2020.106553 · Cited by 406
Graefe, A., & Bohlken, N. (2020) · Media and Communication · doi:10.17645/mac.v8i3.3019 · Cited by 154 · cited 3×
“…from the field of automated journalism suggest that at least these texts do not differ from human-written texts in their perceived credibility (Graefe and Bohlken 2020; Jang et al. 2021; Tandoc et al. 2020; Wölker and Powell 2021) or that machine authorship has only a small negative effect on…”
The impact of text topic and assumed human vs. AI authorship on competence and quality assessment
Proksch, S., Schühle, J., Streeb, E., Weymann, F., Luther, T., & Kimmerle, J. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1412710 · Cited by 17
Self-citation
Research works printed but not linked: 18
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Cited by
Sorted by citations
Mine over Yours: How Authorship Biases Evaluation in Generative Information Retrieval
Ryu, J., Kim, K., Kim, S., Eun, J., Oh, C., & Suh, B. (2026) · Proceedings of the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3805712.3809948
Authorship Attribution and Trust in AI-Generated Scientific Texts: An Experimental Study
Matveev, M., & Matveeva, E. (2026) · Психология. Журнал Высшей школы экономики · doi:10.17323/1813-8918-2026-2-253-262
Metadata
- Authors
- Lermann Henestrosa, A., & Kimmerle, J.
- Affiliations
- Knowledge Construction Lab, Leibniz-Institut für Wissensmedien, 72076 Tübingen, Germany
- Venue
- Journalism and Media
- Publisher
- MDPI AG
- Published
- 2024-08-20
- Volume
- 5
- Issue
- 3
- Pages
- 1085-1097
- ISSN
- 2673-5172
- Cited by
- 53
- References
- 5
- Language
- en
- Funders
- Leibniz-Institut für Wissensmedien (STB Data Science)
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