Identifying the Public's Beliefs About Generative Artificial Intelligence: A Big Data Approach
Why this work is in the frame
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Bibliographic record
Abstract
In an era where generative AI (GenAI) is reshaping industries, public understanding of this phenomenon remains limited. This study addresses this gap by analyzing public beliefs about GenAI using the Technology Acceptance Model and Diffusion of Innovations Theory as frameworks. We adopted a big-data approach, utilizing machine-learning techniques to analyze 21,817 public comments extracted from an initial set of 32,707 on 44 YouTube videos discussing GenAI. Our investigation surfaced six pivotal themes: concerns over job and economic impacts, GenAI's potential to revolutionize problem-solving, its perceived shortcomings in creativity and emotional intelligence, the proliferation of misinformation, existential risks, and privacy decay. Emotion analysis showed that negative emotions dominated at 58.46%, including anger (22.85%) and disgust (17.26%). Sentiment analysis echoed this negativity, with 70% negative. The triangulation of thematic, emotional, and sentiment analyses highlighted a polarized public stance: recognition of GenAI's transformative potential is tempered by significant concerns about its implications. The findings offer actionable insights for engineering managers and policymakers. Strategies such as awareness-building, transparency, public engagement, balanced communication, governance, and human-centered development can address polarization and build trust. Ongoing research into public opinion remains essential for aligning technological advancements with societal expectations and acceptance.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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