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Updated: Jan 10, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

利用大型语言模型来识别与vaping相关的TikTok视频中的参与驱动特征:跨部门研究

Zidian Xie¹, Nanda Kishore Korrapolu², Amisha Dubey³

¹Clinical and Translational Science Institute, University of Rochester, 265 Crittenden Boulevard CU 420708, Rochester, NY, 14642-0708, United States, 1 5852767285.

Journal of medical Internet research

|November 20, 2025

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年轻人更多地参与TikTok关于电子烟的视频,当他们展示汽车,说话,表情符号或幽默时,而不是促销活动. 这些见解可以指导社交媒体上的电子烟预防活动.

科学领域:

社交媒体分析社交媒体分析
公共卫生传播公共卫生传播
数字媒体研究数字媒体研究

背景情况:

电子烟 (e-cigarette) 的使用在美国的年轻人中很普遍.
TikTok是电子烟内容的主要平台,通常包含促销材料.

研究的目的:

确定电子烟TikTok视频的关键特征,这些视频可以提高用户参与度.
在社交媒体上为未来电子烟预防活动的设计提供信息.

主要方法:

分析了1487个电子烟TikTok视频从2023年1月到2024年1月使用TikTok API.
采用GPT-4和Video-LLaMA大型语言模型进行视频特征提取.
使用线性混合效果模型来识别与用户参与相关的特征 (喜欢+分享+评论/观点).

主要成果:

在特征识别方面,GPT-4表现出比视频-LLaMA (24%-88%) 更高的准确性 (83%-100%).
在汽车中设置的视频 (RR=3.91),年轻成年人 (RR=1.24),谈话 (RR=1.63),表情符号 (RR=1.88) 或幽默 (RR=1.61) 显著增加了参与度.
促销电子烟的内容减少了参与度 (RR=0.40).

关键词:

在这里,我们可以看到AIAIAI.在这里,我们可以看到TikTok,TikTok,TikTok.人工智能的人工智能是人工智能.电子烟是一种电子烟.电子烟是一种电子烟.预防预防预防社交媒体社交媒体用户参与度用户参与度电子烟 (vaping) 是一种电子烟.

相关实验视频

Last Updated: Jan 10, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

结论:

特定的TikTok视频元素,包括背景设置,主持人人口统计和内容风格,显著影响用户参与.
研究结果为在TikTok等社交媒体平台上创建有效的电子烟预防内容提供了可操作的见解.