Unbox the Blackbox: Predict and Interpret YouTube Viewership Using Deep Learning
This work addresses the problem of interpretable viewership prediction for content creators and video-sharing sites, aiming to increase model adoption and provide actionable insights for improving viewership and credibility. This is an incremental improvement on existing methods.
This paper proposes Precise Wide and Deep Learning (PrecWD), an interpretable deep learning system designed to predict YouTube video viewership. PrecWD offers precise feature effects and an unstructured component, outperforming benchmarks in health and misinformation video viewership prediction.
Predicting video viewership is a top priority for content creators and video-sharing sites. Content creators live on such predictions to maximize influences and minimize budgets. Video-sharing sites rely on this prediction to promote credible videos and curb violative videos. Although deep learning champions viewership prediction, it lacks interpretability, which is fundamental to increasing the adoption of predictive models and prescribing measurements to improve viewership. Following the design-science paradigm, we propose a novel interpretable IT system, Precise Wide and Deep Learning (PrecWD), to precisely interpret viewership prediction. Improving upon state-of-the-art frameworks, PrecWD offers precise feature effects and designs an unstructured component. PrecWD outperforms benchmarks in two contexts: health video viewership prediction and misinformation viewership prediction. A user study confirms the superior interpretability of PrecWD. This study contributes to IS design theory with generalizable design principles and an interpretable predictive framework. Our findings provide implications to improve video viewership and credibility.