Predicting Session Termination and Retention on X from Fine-Grained Interaction Logs (Student Abstract)

Abstract We study when users end a session on X using high-resolution interaction logs from 215 US participants collected over four weeks. Sessions are defined via data-driven inter-activity gaps, and each session is encoded by fine-grained activity counts and duration (versus a simple activity ratio baseline). Fine-grained activity features substantially outperform the activity ratio baseline (C-index ≈ 0.76 vs. 0.62 for future sessions; 0.72 vs. 0.60 for unseen users), indicating that the composition of activity types is a strong predictor of disengagement. At the app level, we analyze retention over early adoption windows and find that the ratio of active activity in the first three days is most predictive of later usage. These results highlight session composition and early on-platform behavior as practical levers for forecasting and mitigating premature drop-off.