Privacy Concerns in Chatbot Interactions
Open accessPublished version of a preprint
Abstract
Chatbots are increasingly used in a commercial context to make product- or service-related recommendations. By doing so, they collect personal information of the user, similar to other online services. While privacy concerns in an online (website-) context are widely studied, research in the context of chatbot-interaction is lacking. This study investigates the extent to which chatbots with human-like cues influence perceptions of anthropomorphism (i.e., attribution of human-like characteristics), privacy concerns, and consequently, information disclosure, attitudes and recommendation adherence. Findings show that a human-like chatbot leads to more information disclosure, and recommendation adherence mediated by higher perceived anthropomorphism and subsequently, lower privacy concerns in comparison to a machine-like chatbot. This result does not hold in comparison to a website; human-like chatbot and website were perceived as equally high in anthropomorphism. The results show the importance of both mediating concepts in regards to attitudinal and behavioral outcomes when interacting with chatbots.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
Anthropomorphized AI agents lead consumers to treat machines as social agents, increasing data disclosure.
“They lead consumers to treat machines as social agents, increasing data disclosure (Ischen et al. 2020), perceived trustworthiness (Waytz, Heafner, and Epley 2014), and compliance (Adam, Wessel, and Benlian 2021).”
From Emotional Manipulation by AI Companions (2025) · cites this paper as Ischen et al. 2020
The paper’s abstract reports that a human-like chatbot elicited more information disclosure than a machine-like chatbot, mediated by greater perceived anthropomorphism and lower privacy concerns. The Introduction describes users attributing human-like characteristics to chatbots. The citing claim extends this finding to anthropomorphized AI agents generally, although the paper says the disclosure result did not hold when the comparator was a website. (LLM, conf=0.83)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
On seeing human: A three-factor theory of anthropomorphism
Epley, N., Waytz, A., & Cacioppo, J. T. (2007) · Psychological Review · doi:10.1037/0033-295x.114.4.864 · Cited by 4,069 · see citing works
Machines and Mindlessness: Social Responses to Computers
Nass, C., & Moon, Y. (2000) · Journal of Social Issues · doi:10.1111/0022-4537.00153 · Cited by 4,022 · see citing works
Mere Exposure: A Gateway to the Subliminal
Zajonc, R. B. (2001) · Current Directions in Psychological Science · doi:10.1111/1467-8721.00154 · Cited by 1,299 · see citing works
Go, E., & Sundar, S. S. (2019) · Computers in Human Behavior · doi:10.1016/j.chb.2019.01.020 · Cited by 1,089 · see citing works
Anthropomorphism of computers: Is it mindful or mindless?
Kim, Y., & Sundar, S. S. (2012) · Computers in Human Behavior · doi:10.1016/j.chb.2011.09.006 · Cited by 462 · see citing works
Machine Heuristic: When We Trust Computers More than Humans with Our Personal Information
Sundar, S. S., & Kim, J. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300768 · Cited by 347 · see citing works
Predicting consumer responses to a chatbot on facebook
Zarouali, B., Van den Broeck, E., Walrave, M., & Poels, K. (2018) · Cyberpsychology, Behavior, and Social Networking · doi:10.1089/cyber.2017.0518 · Cited by 229 · see citing works
Crutzen, R., Peters, G.-J. Y., Portugal, S. D., Fisser, E. M., & Grolleman, J. J. (2011) · Journal of Adolescent Health · doi:10.1016/j.jadohealth.2010.09.002 · Cited by 205 · see citing works
Voices in and of the machine: Source orientation toward mobile virtual assistants
Guzman, A. L. (2019) · Computers in Human Behavior · doi:10.1016/j.chb.2018.08.009 · Cited by 199 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Creepiness: Its antecedents and impact on loyalty when interacting with a chatbot
Rajaobelina, L., Prom Tep, S., Arcand, M., & Ricard, L. (2021) · Psychology & Marketing · doi:10.1002/mar.21548 · Cited by 252 · see citing works
Kumar, J. A. (2021) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-021-00302-w · Cited by 183 · see citing works
Wang, H., Gupta, S., Singhal, A., Muttreja, P., Singh, S., Sharma, P., & Piterova, A. (2022) · Journal of Medical Internet Research · doi:10.2196/29969 · Cited by 111 · see citing works
Zhang, Z., Jia, M., Lee, H.-P. (., Yao, B., Das, S., Lerner, A., Wang, D., & Li, T. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642385 · Cited by 95 · see citing works
Can Generative AI Chatbots Emulate Human Connection? A Relationship Science Perspective
Smith, M. G., Bradbury, T. N., & Karney, B. R. (2025) · Perspectives on Psychological Science · doi:10.1177/17456916251351306 · Cited by 88 · see citing works
Song, M., Zhu, Y., Xing, X., & Du, J. (2024) · Behaviour & Information Technology · doi:10.1080/0144929x.2023.2285943 · Cited by 49 · see citing works
Thematic Analysis on User Reviews for Depression and Anxiety Chatbot Apps: Machine Learning Approach
Ahmed, A., Aziz, S., Khalifa, M., Shah, U., Hassan, A., Abd-Alrazaq, A., & Househ, M. (2022) · JMIR Formative Research · doi:10.2196/27654 · Cited by 37 · see citing works
Emotional Manipulation by AI Companions
De Freitas, J., Oguz-Uguralp, Z., & Kaan-Uguralp, A. (2025) · arXiv · doi:10.48550/arxiv.2508.19258 · Cited by 12 · see citing works
Privacy Perceptions of Custom GPTs by Users and Creators
Ma, R., Maidhof, C., Carrillo, J. C., Lindqvist, J., & Such, J. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713540 · Cited by 8 · see citing works
Understanding Users' Privacy Perceptions Towards LLM's RAG-based Memory
Zhang, S., Ma, R., Ma, Y., Li, S., Xu, Y., Yi, X., & Li, H. (2025) · Proceedings of the 2025 Workshop on Human-Centered AI Privacy and Security · doi:10.1145/3733816.3760750 · Cited by 6 · see citing works
- Authors
- Ischen, C., Araujo, T., Voorveld, H., van Noort, G., & Smit, E.
- Affiliations
- University of Amsterdam (NL)
- Corresponding author
- Ischen, Carolin
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2020
- Pages
- 34-48
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2999685709
- Cited by
- 152
- References
- 9
- Citations by year
- 2026: 22 · 2025: 28 · 2024: 34 · 2023: 28 · 2022: 22 · 2021: 13 · 2020: 5
- FWCI
- 6.49
- Type
- conference paper
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nc-nd
- www.springer.com/tdm (tdm)
- Locations
- Lecture notes in computer science (publishedVersion)
- UvA-DARE (University of Amsterdam) (submittedVersion, other-oa)
- SocArXiv (OSF Preprints) (submittedVersion, cc-by-nc-nd)
- SocArXiv (OSF Preprints) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Keywords
- chatbots, privacy concerns, anthropomorphism, information disclosure, recommender systems, user attitudes
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 ›
Are you an author, or do you see a problem with this page? Request a review
