计算机模型为烟草研究提供信息的新时代的生成人工智能:简短的报告
Julia Vassey1, Chris J Kennedy2,3, Ho-Chun Herbert Chang4
1Department of Population and Public Health Sciences, University of Southern California, Los Angeles, California, USA vassey@usc.edu.
Tobacco control
|January 30, 2025
概括
生成性AI准确地检测到社交媒体上的电子烟促销活动,识别出与大麻,时尚和健康生活方式的联系. 这项技术通过分析大量数据集来分析针对年轻人的营销策略,帮助烟草监管科学 (TRS).
科学领域:
- 烟草监管科学 烟草监管科学
- 人工智能在公共卫生中的作用
- 数字营销分析 数字营销分析
背景情况:
- 社交媒体影响者使用可能降低年轻人风险感知的策略来推广电子烟.
- 营销往往将电子烟的内容与健康的生活方式或娱乐的图像结合起来.
- 对烟草监管科学 (TRS) 来说,监测此类内容至关重要.
研究的目的:
- 评估生成AI在社交媒体上检测电子烟促销方面的有效性.
- 评估电子烟推广与大麻,时尚和健康生活方式等主题的关联.
- 确定AI在TRS中大规模数据分析的实用性.
主要方法:
- 从2021-2024年分析了102个Instagram和TikTok视频,其中包括电子烟促销活动.
- 利用OpenAI的GPT-4o多式模式进行零射击物体检测.
- 模型确定了尼古丁电子烟,大麻电子烟,时尚,娱乐和健康生活方式的主题.
主要成果:
- 实现了高准确率:尼古丁蒸汽87%;大麻蒸汽96% .
- 在主题检测方面表现出色:时尚99%,娱乐96%,健康生活方式98%.
- 生成型人工智能模型在没有额外的培训的情况下表现出强大的性能.
结论:
- 生成性人工智能使得准确的,零射击对象检测用于社交媒体内容分析.
- 这种人工智能方法可以处理大数据以识别电子烟和与物质有关的促销活动.
- 通过揭示促销背景,研究结果为烟草监管科学 (TRS) 提供了有价值的数据.
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