  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.

Machine Learning;Artificial Intelligence