The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Risks and protective measures for synthetic relationships
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Recent advancements in text-generative AI have produced social chatbots capable of romantic and sexual interactions so sophisticated that users often cannot distinguish them from human conversation partners, enabling these systems to elicit genuine emotional responses and attachment (Skjuve et al., 2021;Kim et al., 2023;Starke et al., 2024).”
From Understanding Human-Chatbot Romance: A Qualitative and Quantitative Study on Romantic Fantasy and Other Interpersonal Characteristics (2025) · cites this paper as Starke et al., 2024
Citations
Cites
Li, H., Zhang, R., Lee, Y.-C., Kraut, R. E., & Mohr, D. C. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00979-5 · Cited by 598
My AI Friend: How Users of a Social Chatbot Understand Their Human–AI Friendship
Brandtzaeg, P. B., Skjuve, M., & Følstad, A. (2022) · Human Communication Research · doi:10.1093/hcr/hqac008 · Cited by 490
The potential of generative AI for personalized persuasion at scale
Matz, S. C., Teeny, J. D., Vaid, S. S., Peters, H., Harari, G. M., & Cerf, M. (2024) · Scientific Reports · doi:10.1038/s41598-024-53755-0 · Cited by 315
The Potential of Generative AI for Personalized Persuasion at Scale
Matz, S., Teeny, J., Vaid, S. S., Peters, H., Harari, G. M., & Cerf, M. (2023) · doi:10.31234/osf.io/rn97c · Cited by 312
The Ethics of Advanced AI Assistants
Gabriel, I., Manzini, A., Keeling, G., Hendricks, L. A., Rieser, V., Iqbal, H., Tomašev, N., Ktena, I., Kenton, Z., Rodriguez, M., et al. (2024) · arXiv · doi:10.48550/arxiv.2404.16244 · Cited by 57
Research works printed but not linked: 5
- Clabaugh, C. & Matarić, M. Sci. Robot. 3, eaat7451 (2018). · DOI
- Sharma, A., Lin, I. W., Miner, A. S., Atkins, D. C. & Althoff, T. Nat. Mach. Intell. 5, 46–57 (2023). · DOI · Paper page
- Szczuka, J. M., Strathmann, C., Szymczyk, N., Mavrina, L. & Krämer, N. C. Int. J. Child Comput. Interact. 33, 100460 (2022). · DOI
- Köbis, N., Bonnefon, J.-F. & Rahwan, I. Nat. Hum. Behav. 5, 679–685 (2021). · DOI
- Hutson, M. Nature 591, 22–25 (2021). · DOI
Cited by
Moore, J., Grabb, D., Agnew, W., Klyman, K., Chancellor, S., Ong, D. C., & Haber, N. (2025) · Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3715275.3732039 · Cited by 204
Adewale, M. D., & Muhammad, U. I. (2025) · Journal of Technology in Behavioral Science · doi:10.1007/s41347-025-00549-4 · Cited by 46
Generative artificial intelligence–mediated confirmation bias in health information seeking
Lopez‐Lopez, E., Abels, C. M., Holford, D., Herzog, S. M., & Lewandowsky, S. (2025) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.15413 · Cited by 38
Ebner, P., & Szczuka, J. (2025) · arXiv · doi:10.48550/arxiv.2503.00195 · Cited by 33
Self-citationYang, X. (2026) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2026.2618548 · Cited by 3
Schimmelpfennig, R., Díaz, M., Prabhakaran, V., & Davani, A. (2025) · arXiv · doi:10.48550/arxiv.2512.17898 · Cited by 2
“Prior work hypothesizes that such dynamics may, in turn, heighten user vulnerability to targeted persuasion, emotional attachment, and over-reliance on AI systems for high-stakes tasks for which the technology remains ill-suited [6, 19–21].”
Metadata
- Authors
- Starke, C., Ventura, A., Bersch, C., Cha, M., de Vreese, C., Doebler, P., Dong, M., Krämer, N., Leib, M., Peter, J., Schäfer, L., Soraperra, I., Szczuka, J., Tuchtfeld, E., Wald, R., & Köbis, N.
- ORCID
- Venue
- Nature Human Behaviour
- Published
- 2024-10-22
- Volume
- 8
- Issue
- 10
- Pages
- 1834-1836
- ISSN
- 2397-3374
- Cited by
- 44
- References
- 5
- Language
- en
- Licence
- www.springernature.com/gp/researchers/text-and-data-mining (tdm)
- www.springernature.com/gp/researchers/text-and-data-mining (vor)
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
