Influencer marketing has become a very popular tool to reach customers. Despite the rapid growth in influencer videos, there has been little research on the effectiveness of their constituent elements in explaining video engagement. We study YouTube influencers and analyze their unstructured video data across text, audio and images using a novel "interpretable deep learning" framework that accomplishes both goals of prediction and interpretation. Our prediction-based approach analyzes unstructured data and finds that "what is said" in words (text) is more influential than "how it is said" in imagery (images) followed by acoustics (audio). Our interpretation-based approach is implemented after completion of model prediction by analyzing the same source of unstructured data to measure importance attributed to the video elements. We eliminate several spurious and confounded relationships, and identify a smaller subset of theory-based relationships. We uncover novel findings that establish distinct effects for measures of shallow and deep engagement which are based on the dual-system framework of human thinking. Our approach is validated using simulated data, and we discuss the learnings from our findings for influencers and brands.
翻译:影响者营销已成为一种非常受欢迎的接触客户的工具。 尽管影响力人物视频的迅速增长,但对其组成要素在解释视频参与方面的有效性的研究却很少。 我们用一个创新的“解释深刻的学习”框架来研究YouTube影响者,并分析其在文本、音频和图像方面的非结构化视频数据,这个框架实现了预测和解释的目标。我们的预测方法分析非结构化的数据,发现在文字(文字)中“所言”比在图像(图像)中“所言”更有影响力,然后是声学(音频)中。我们在完成模型预测后,通过分析同一非结构化数据来源来衡量视频要素的重要性,从而实施我们基于解释的方法。我们消除了几个虚伪和混乱的关系,并找出了一小部分基于理论的关系。我们发现的新发现,根据人类思维的双重系统框架,为浅层次和深度接触的措施确定了明显的效果。我们的方法通过模拟数据加以验证,我们讨论了从影响者和品牌中得出的研究结果。