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.

翻译：在线社交平台的成功依赖于其大规模预测和理解用户行为的能力。本文提出的数据表明，上下文感知建模方法可能为在线社交平台的用户参与度提供一种既全面又轻量、且具有潜在隐私保护特性的表示方式。通过利用深度LSTM神经网络分析来自近8万名用户的超过1亿个Snapchat会话，我们证明主动和被动使用模式可从历史行为中预测（R²=0.345），且集成上下文信息能显著提升预测性能，优于仅基于行为的基线模型（R²=0.522）。研究发现，与从应用内行为历史中提取的特征相比，智能手机连接状态、地理位置、时间上下文和天气等相关特征能够捕捉用户参与度中非冗余的方差。进一步，我们证明若考虑即时上下文信息，仅需极少量的行为历史数据即可解释大部分方差（R²=0.44）。这些结果表明，上下文感知方法通过减少对长数据历史记录的依赖，在提升模型效率与隐私保护方面具有潜力。最后，我们运用模型可解释性技术初步探究了潜在的行为机制。研究结果与上下文依赖、习惯驱动的主动和被动使用模式概念一致，强调了用户行为上下文化表示对于预测社交平台用户参与度的价值。