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 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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".