When Human-AI Interactions Become Parasocial: Agency and Anthropomorphism in Affective Design
Bibliographic record
Abstract
With the continuous improvement of large language models (LLMs), chatbots can produce coherent and continuous word sequences that mirror natural human language. While the use of natural language and human-like conversation styles enables the use of chatbots within a range of everyday settings, these usability-enhancing features can also have unintended consequences, such as making fallible information seem trustworthy by emphasizing friendliness and closeness. This can have serious implications for information retrieval tasks performed with chatbots. In this paper, we provide an overview of the literature on parasociality, social affordance, and trust to bridge these concepts within human-AI interactions. We critically examine how chatbot “roleplaying” and user role projection co-produce a pseudo-interactive, technologically-mediated space with imbalanced dynamics between users and chatbots. Based on the review of the literature, we develop a conceptual framework of parasociality in chatbots that describes interactions between humans and anthropomorphized chatbots. We dissect how chatbots use personal pronouns, conversational conventions, affirmations, and similar strategies to position the chatbots as users’ companions or assistants, and how these tactics induce trust-forming behaviors in users. Finally, based on the conceptual framework, we outline a set of ethical concerns that emerge from parasociality, including illusions of reciprocal engagement, task misalignment, and leaks of sensitive information. This paper argues that these possible consequences arise from a positive feedback cycle wherein anthropomorphized chatbot features encourage users to fill in the context around predictive outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".