用CLIP分析4万张大麻食品图，发现食物视觉元素越明显，用户互动越高
Detecting Visual Triggers in Cannabis Imagery: A CLIP-Based Multi-Labeling Framework with Local-Global Aggregation
- 基于CLIP模型融合局部与全局特征，实现多标签图像识别
- 食品类视觉元素（如水果、糖果）与用户互动正相关，色彩越艳越低
- 适合政策制定者参考，用于设计大麻食品警示标签和营销规范
本研究探讨了在线讨论中大麻食品的视觉与文本特征及其对用户参与度的影响。利用CLIP模型分析2021年3月1日至8月31日来自Facebook的42,743张图片，重点关注食物相关视觉元素，并考察颜色鲜艳度和亮度等图像属性对用户互动的影响。文本分析采用BART模型作为去噪自编码器，对结构主题建模生成的十个主题进行分类，探索其与用户参与度的关系。线性回归分析显示，食物相关视觉元素（如水果、糖果、烘焙食品）与用户参与度存在显著正相关；同时，大麻合法化等文本主题也与参与度正相关。相反，图像色彩鲜艳度及某些文本主题与参与度呈负相关。研究结果为政策制定者和监管机构设计警告标签与营销规范提供了可操作的洞见，以应对娱乐性大麻食品可能带来的风险。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。