Predicting and interpreting digital platform survival: An interpretable machine learning approach

Xinyu Zhu, Qiang Zhang, Baojun Ma

Abstract: Highlights•Despite the substantial economic impact of digital platforms, research on platform risk evaluation has been sparse.•We investigate whether online content can serve as a leading indicator of digital platform survival.•We employ machine learning techniques to extract features from three types of online content.•Using a predictive XGBoost algorithm, we find online content is a strong predictor of platform survival.•We provide practical implications for market managers and platform owners.