Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.08648 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909425206296576 |
|---|---|
| author | Lu, Linqi Yu, Xianshi Reddy, Akhil Perumal |
| author_facet | Lu, Linqi Yu, Xianshi Reddy, Akhil Perumal |
| contents | This study investigates the interplay of visual and textual features in online discussions about cannabis edibles and their impact on user engagement. Leveraging the CLIP model, we analyzed 42,743 images from Facebook (March 1 to August 31, 2021), with a focus on detecting food-related visuals and examining the influence of image attributes such as colorfulness and brightness on user interaction. For textual analysis, we utilized the BART model as a denoising autoencoder to classify ten topics derived from structural topic modeling, exploring their relationship with user engagement. Linear regression analysis identified significant positive correlations between food-related visuals (e.g., fruit, candy, and bakery) and user engagement scores, as well as between engagement and text topics such as cannabis legalization. In contrast, negative associations were observed with image colorfulness and certain textual themes. These findings offer actionable insights for policymakers and regulatory bodies in designing warning labels and marketing regulations to address potential risks associated with recreational cannabis edibles. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_08648 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Detecting Visual Triggers in Cannabis Imagery: A CLIP-Based Multi-Labeling Framework with Local-Global Aggregation Lu, Linqi Yu, Xianshi Reddy, Akhil Perumal Computers and Society Computer Vision and Pattern Recognition This study investigates the interplay of visual and textual features in online discussions about cannabis edibles and their impact on user engagement. Leveraging the CLIP model, we analyzed 42,743 images from Facebook (March 1 to August 31, 2021), with a focus on detecting food-related visuals and examining the influence of image attributes such as colorfulness and brightness on user interaction. For textual analysis, we utilized the BART model as a denoising autoencoder to classify ten topics derived from structural topic modeling, exploring their relationship with user engagement. Linear regression analysis identified significant positive correlations between food-related visuals (e.g., fruit, candy, and bakery) and user engagement scores, as well as between engagement and text topics such as cannabis legalization. In contrast, negative associations were observed with image colorfulness and certain textual themes. These findings offer actionable insights for policymakers and regulatory bodies in designing warning labels and marketing regulations to address potential risks associated with recreational cannabis edibles. |
| title | Detecting Visual Triggers in Cannabis Imagery: A CLIP-Based Multi-Labeling Framework with Local-Global Aggregation |
| topic | Computers and Society Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.08648 |