Context-Aware Prediction of User Engagement on Online Social Platforms
Heinrich Peters Yozen Liu Francesco Barbieri Raiyan A. Baten Sandra C. Matz Maarten W. Bos
The success of online social platforms hinges on their ability to predict and
understand user behavior at scale. Here, we present data suggesting that
context-aware modeling approaches may offer a holistic yet lightweight and
potentially privacy-preserving representation of user engagement on online
social platforms. Leveraging deep LSTM neural networks to analyze more than 100
million Snapchat sessions from almost 80.000 users, we demonstrate that
patterns of active and passive use are predictable from past behavior
(R2=0.345) and that the integration of context information substantially
improves predictive performance compared to the behavioral baseline model
(R2=0.522). Features related to smartphone connectivity status, location,
temporal context, and weather were found to capture non-redundant variance in
user engagement relative to features derived from histories of in-app
behaviors. Further, we show that a large proportion of variance can be
accounted for with minimal behavioral histories if momentary context
information is considered (R2=0.44). These results indicate the potential of
context-aware approaches for making models more efficient and
privacy-preserving by reducing the need for long data histories. Finally, we
employ model explainability techniques to glean preliminary insights into the
underlying behavioral mechanisms. Our findings are consistent with the notion
of context-contingent, habit-driven patterns of active and passive use,
underscoring the value of contextualized representations of user behavior for
predicting user engagement on social platforms.