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Voices of the crowd: Exploring user evaluations of ChatGPT using structural topic modeling
Kai Ding1, Yan Yang2, Dongwei Zhao3
1School of Business Administration, Ningbo University of Finance and Economics, Ningbo, China.
Public perception of ChatGPT (Chat Generative Pre-trained Transformer) is divided, with users valuing its efficiency but fearing job displacement and factual errors. Direct experiences, not abstract ideas, shape user attitudes toward generative AI.
Area of Science:
- Human-Computer Interaction
- Computational Social Science
- Artificial Intelligence Ethics
Background:
- Public understanding of advanced AI like ChatGPT is limited.
- Existing research inadequately documents user perceptions of AI capabilities and societal impact.
Purpose of the Study:
- To analyze public perceptions of ChatGPT using large-scale YouTube comments.
- To understand user evaluations of ChatGPT's capabilities, limitations, and societal implications.
Main Methods:
- Computational mixed-methods approach.
- Sentiment analysis and Structural Topic Modeling on YouTube comments (2023-2025).
Main Results:
- User interpretations of AI behavior differ from technical definitions, with bias seen as ideological.
- A utility-anxiety tension exists: praise for efficiency (programming, writing) vs. concerns about reliability, authenticity, and job displacement.
- Negative AI evaluations arise from specific errors, positive ones from clear benefits.
Conclusions:
- User attitudes toward generative AI are shaped by direct experiences and specific circumstances.
- Findings offer insights for aligning AI design, governance, and risk communication with user experiences.
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