解析 YouTube 网红视频中的参与度：一种基于注意力的方法
Unboxing Engagement in YouTube Influencer Videos: An Attention-Based Approach
AI总结：
本文提出基于注意力的可解释深度学习框架，融合文本、音频和视频图像预测 YouTube 长视频参与度，发现文本最重要，且前30秒听觉与评论情绪、视觉与赞踩情绪分别相关。
摘要：
网红营销已成为触达消费者的一种广泛使用的策略。尽管网红和品牌合作方对预测网红视频的参与度兴趣日益增长，但关于不同视频数据模态在预测参与度时的相对重要性，相关研究仍然很少。我们使用一种可解释的深度学习框架，分析来自长形式 YouTube 网红视频的非结构化数据，涵盖文本、音频和视频图像；该框架利用模型对视频元素的注意力。这一框架能够实现较强的样本外预测，随后采用一种剪枝虚假关联的新方法进行事后解释。我们基于预测的结果表明，在预测视频参与度时，通过词语（文本）表达的“说了什么”，比通过影像（视频图像）或声学特征（音频）呈现的“如何说”更重要。基于解释的发现显示，在视频的关键起始阶段（前 30 秒），听觉刺激（例如品牌提及和音乐）与语言参与（评论）中表达的情绪相关，而视觉刺激（例如人物和包装商品的视频图像）则与通过非语言参与（赞/踩比例）表达的情绪相关。我们通过多种方法验证了所提方法，将研究发现与相关理论联系起来，并讨论了其对网红、品牌和代理机构的启示。
英文摘要：
Influencer marketing has become a widely used strategy for reaching customers. Despite growing interest among influencers and brand partners in predicting engagement with influencer videos, there has been little research on the relative importance of different video data modalities in predicting engagement. We analyze unstructured data from long-form YouTube influencer videos - spanning text, audio, and video images - using an interpretable deep learning framework that leverages model attention to video elements. This framework enables strong out-of-sample prediction, followed by ex-post interpretation using a novel approach that prunes spurious associations. Our prediction-based results reveal that "what is said" through words (text) is more important than "how it is said" through imagery (video images) or acoustics (audio) in predicting video engagement. Interpretation-based findings show that during the critical onset period of a video (first 30 seconds), auditory stimuli (e.g., brand mentions and music) are associated with sentiment expressed in verbal engagement (comments), while visual stimuli (e.g., video images of humans and packaged goods) are linked with sentiment expressed through non-verbal engagement (the thumbs-up/down ratio). We validate our approach through multiple methods, connect our findings to relevant theory, and discuss implications for influencers, brands and agencies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
