Unbox the Black-Box: Predict and Interpret YouTube Viewership Using Deep Learning

• 成果类型: Article
• 署名作者: Xie, Jiaheng; Chai, Yidong; Liu, Xiao
• 署名单位: University of Delaware; Hefei University of Technology; Arizona State University; Arizona State University-Tempe; Hefei University of Technology
• 刊物名称: JOURNAL OF MANAGEMENT INFORMATION SYSTEMS
• ISSN/ISSBN: 0742-1222
• DOI: 10.1080/07421222.2023.2196780
• 发表日期: 2023
• 页码: 541-579
• 关键词: word-of-mouth; Social media; design science; analytics; identification; KNOWLEDGE; FRAMEWORK; Sentiment; care

摘要

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.