# Context-Aware Prediction of User Engagement on Online Social Platforms

arXiv: https://arxiv.org/abs/2310.14533 · HTML: https://mlsanity.com/paper/2310.14533/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 108 references that are in the map.
- First posted on arXiv 23 Oct 2023.

## Builds on (most cited first, 10 of 10)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [BOHB: Robust and Efficient Hyperparameter Optimization at Scale](https://mlsanity.com/paper/1807.01774/) (2018)
- [Predictive Business Process Monitoring with LSTM Neural Networks](https://mlsanity.com/paper/1612.02130/) (2016)
- [Deep Learning with Long Short-Term Memory for Time Series Prediction](https://mlsanity.com/paper/1810.10161/) (2018)
- [Large Language Models Can Infer Psychological Dispositions of Social Media Users](https://mlsanity.com/paper/2309.08631/) (2023)
- [Characterizing and Forecasting User Engagement with In-app Action Graph: A Case Study of Snapchat](https://mlsanity.com/paper/1906.00355/) (2019)
- [Latent Human Traits in the Language of Social Media: An Open-Vocabulary Approach](https://mlsanity.com/paper/1705.08038/) (2017)
- [Slower is Better: Revisiting the Forgetting Mechanism in LSTM for Slower Information Decay](https://mlsanity.com/paper/2105.05944/) (2021)
- [A Cross-Platform Collection of Social Network Profiles](https://mlsanity.com/paper/1607.03274/) (2016)

## Built on by (most cited first, 1 of 1)

- [Social Media Use is Predictable from App Sequences: Using LSTM and Transformer Neural Networks to Model Habitual Behavior](https://mlsanity.com/paper/2404.16066/) (2024)

## Abstract (arXiv)

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 features 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 is considered (R2=0.442). 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.