Unveiling Chatbot APP Characters: A Socio-Cultural Analysis of App Representations and User Perceptions
Bibliographic record
Abstract
This study explores the socio-cultural logics underlying the character construction of chatbot applications by examining how developers strategically design and represent chatbot personas to align with users' emotional expectations and normative imaginaries.Based on a dataset of 110 chatbot apps collected from Google Play, it employs a mixed-methods approach that includes keyword frequency analysis, co-occurrence network visualization using Gephi, and iconographic analysis.Chatbot character attributes are categorized into three dimensions: image, identity, and function.Findings indicate a predominant emphasis on anthropomorphic features, particularly the combination of "human," "social," and "friend," suggesting that developers prioritize the construction of emotionally available yet non-intimate chatbot companions.While utilitarian roles such as "assistant" or "knowledge provider" appear less frequently, social identities like "friend" or "roleplay" are foregrounded and often visually reinforced through gendered and sometimes sexualized icon designs.Drawing on social constructivism and sociomateriality, this study argues that chatbot character design reflects developers' projections of the "ideal user," shaped by existing social norms and stereotypes.By centring "similarity" as a key design logic, these representations risk reproducing cultural biases, particularly those surrounding gender and emotional labour, under the guise of user familiarity.The study concludes by emphasizing the need for more critical and reflexive design practices that challenge rather than reinforce dominant socio-technical imaginaries embedded in human-AI interaction.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".