ABSTRACT

As video-sharing sites emerge as a critical part of the social media landscape, video viewership prediction becomes essential for content creators and businesses to optimize influence and marketing outreach with minimum budgets. Although deep learning champions viewership prediction, it lacks interpretability, which is required by regulators and is fundamental to the prioritization of the video production process and promoting trust in algorithms. Existing interpretable predictive models face the challenges of imprecise interpretation and negligence of unstructured data. Following the design-science paradigm, we propose a novel Precise Wide-and-Deep Learning (PrecWD) to accurately predict viewership with unstructured video data and well-established features while precisely interpreting feature effects. PrecWD’s prediction outperforms benchmarks in two case studies and achieves superior interpretability in two user studies. We contribute to IS knowledge base by enabling precise interpretability in video-based predictive analytics and contribute nascent design theory with generalizable model design principles. Our system is deployable to improve video-based social media presence.

摘要

随着视频共享网站成为社交媒体领域的重要组成部分，视频收视率预测对于内容创作者和企业以最低预算优化影响力和营销推广至关重要。尽管深度学习支持收视率预测，但它缺乏可解释性，而可解释性是监管机构所要求的，也是确定视频制作过程的优先级和促进对算法的信任的基础。现有的可解释预测模型面临着解释不精确和忽视非结构化数据的挑战。遵循设计科学范式，我们提出了一种新颖的精确广度和深度学习（PrecWD），可以利用非结构化视频数据和成熟的特征准确预测收视率，同时精确解释特征效果。PrecWD 的预测在两个案例研究中优于基准，并在两个用户研究中实现了卓越的可解释性。我们通过在基于视频的预测分析中实现精确解释来为 IS 知识库做出贡献，并通过可推广的模型设计原则贡献新生的设计理论。我们的系统可部署以改善基于视频的社交媒体表现。