AI Writing Assistants Influence Topic Choice in Self-Presentation
Open accessAbstract
AI language technologies increasingly assist and expand human communication. While AI-mediated communication reduces human effort, its societal consequences are poorly understood. In this study, we investigate whether using an AI writing assistant in personal self-presentation changes how people talk about themselves. In an online experiment, we asked participants (N=200) to introduce themselves to others. An AI language assistant supported their writing by suggesting sentence completions. The language model generating suggestions was fine-tuned to preferably suggest either interest, work, or hospitality topics. We evaluate how the topic preference of a language model affected users’ topic choice by analyzing the topics participants discussed in their self-presentations. Our results suggest that AI language technologies may change the topics their users talk about. We discuss the need for a careful debate and evaluation of the topic priors built into AI language technologies.
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?
AI assistance can shift the characteristics of writing content, such as a more positive tone, or even affect topics addressed in writing.
“Indeed, various AIMC studies have shown that AI assistance (e.g., word-by-word suggestions or short-reply suggestions) can shift the characteristics of writing content, such as a more positive tone [22,48,59], or even affect topics addressed in writing [57].”
From 'It was 80% me, 20% AI': Seeking Authenticity in Co-Writing with Large Language Models (2025) · cites this paper as [57]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 25 · Cited by 30
Research works printed but not linked: 13
- Advait Bhat, Saaket Agashe, Niharika Mohile, Parth Oberoi, Ravi Jangir, and Anirudha Joshi. 2022. Studying writer-suggestion interaction: A qualitative study to understand writer interaction with aligned/misaligned next-phrase suggestion. (2022). Publisher: arXiv.
- Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020. Language models are few-shot learners. Advances in neural information processing systems 33 (2020), 1877-1901.
- M. A. DeVito, J. Birnholtz, J. T. Hancock, and "Platforms. 2017. people. and perception: Using afordances to understand self-presentation on social media" in Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing. 740-754 pages.
- Jefrey T Hancock, Mor Naaman, and Karen Levy. 2020. AI-mediated commu- nication: Defnition, research agenda, and ethical considerations. Journal of Computer-Mediated Communication 25, 1 (2020), 89-100.
- Maurice Jakesch, Jefrey Hancock, and Mor Naaman. 2022. Human Heuristics for AI-Generated Language Are Flawed. arXiv preprint arXiv:2206.07271 (2022). · arXiv
- Xiao Ma, Jefery T. Hancock, Kenneth Lim Mingjie, and Mor Naaman. 2017. Self- Disclosure and Perceived Trustworthiness of Airbnb Host Profles. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (Portland, Oregon, USA) (CSCW '17). Association for Computing
- Alec Radford, Jefrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019. Language models are unsupervised multitask learners. OpenAI blog 1, 8 (2019), 9.
- Dietram A Scheufele. 2000. Agenda-setting, priming, and framing revisited: Another look at cognitive efects of political communication. Mass communication & society 3, 2-3 (2000), 297-316.
- B. R. Schlenker. 2012. Self-presentation in Handbook of Self and Identity. 2nd Ed, (The Guilford Press. 542-570 pages.
- Emma Strubell, Ananya Ganesh, and Andrew McCallum. 2019. Energy and policy considerations for deep learning in NLP. arXiv preprint arXiv:1906.02243 (2019). · arXiv
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 (2017).
- Genta Indra Winata, Andrea Madotto, Zhaojiang Lin, Rosanne Liu, Jason Yosinski, and Pascale Fung. 2021. Language models are few-shot multilingual learners. arXiv preprint arXiv:2109.07684 (2021). · arXiv
- Rowan Zellers, Ari Holtzman, Hannah Rashkin, Yonatan Bisk, Ali Farhadi, Franziska Roesner, and Yejin Choi. 2019. Defending against neural fake news. Advances in neural information processing systems 32 (2019).
- Authors
- Poddar, R., Sinha, R., Naaman, M., & Jakesch, M.
- Affiliations
- Cornell Tech (US)
- Cornell University (US)
- Venue
- Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2023-04-19
- Pages
- 1-6
- OpenAlex
- W4366590749
- Cited by
- 30
- References
- 25
- Citations by year
- 2026: 13 · 2025: 11 · 2024: 6
- FWCI
- 7.52
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) (publishedVersion)
- Funders
- NSF (National Science Foundation) (CHS 1901151/1901329)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Keywords
- AI writing tools, self-presentation, topic selection, AI language models, AI-mediated communication, fine-tuning, self-disclosure
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
