Understanding Public Responses to AI-generated Visual Art: A Topic and Emotion Analysis of Social Media Comments Data
Bibliographic record
Abstract
This study analyzes the comments on AI-generated visual art on social media, focusing on emotional and thematic aspects. With the rise of AI-generated content on social media platforms such as DALL·E and Midjourney, AI art has gained both positive and negative attention. This mixed-methods study uses emotion analysis and thematic modeling to investigate the audience's response to the art of AI-generation. The study uses two sets of frameworks: Machine-Driven Classification of Open-Ended Responses (MDCOR) and Sentiment and Emotion Network Analysis (SENA) to classify and analyze social media comments to grasp the emotional tone of the discussion and AI art-related issues. The data shows that the social media debate about authenticity, author identity and whether AI will replace human artists presents a complex picture of enthusiasm, doubt and worry. The study also found that the public's impression will change in the process of interacting with AI art. This study provides a comprehensive framework for understanding public opinion and lays the foundation for further investigation of the social and cultural impact of AI on art and creativity.
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How this classification was reachedexpand
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".