“That is scary!”: consumer perceptions and discourses on ChatGPT
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose The rise of conversational artificial intelligence (AI) bots such as ChatGPT highlights users’ anxieties and high expectations. This study aims to explore consumers’ views of AI conversational bots and examines their societal implications, emphasizing public perception as a fundamental factor in their acceptance and integration. Design/methodology/approach This study combines manual and automated thematic analysis to understand public sentiment by analyzing 45,844 YouTube comments. The comments are collected from the top five nonsponsored English-language YouTube videos on ChatGPT, with comments extracted using Octoparse. Key themes and their relationships are identified through manual coding and further analyzed using Leximancer to enhance the depth and accuracy of the analysis by detecting patterns in large data sets. Findings The analysis reveals three primary areas: empowerment through AI-enhanced capabilities, anxiety over AI-induced societal shifts and negotiating human–AI collaboration. Concerns are expressed about misinformation, privacy and the impact of AI on employment and human skills. Conversely, positive perceptions highlight AI’s role in education, personal productivity and medical diagnosis. These themes categorize public sentiment into techno-skepticism, techno-realism and techno-optimism, demonstrating the complex and diverse opinions on AI technology. Originality/value This research bridges AI’s technical aspects with its social and ethical dimensions, providing a comprehensive understanding of public sentiment towards ChatGPT. It underscores the importance of examining consumer views as a foundational step in understanding AI’s broader societal impacts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 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 it