# DeepMood: Modeling Mobile Phone Typing Dynamics for Mood Detection

arXiv: https://arxiv.org/abs/1803.08986 · HTML: https://mlsanity.com/paper/1803.08986/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2018.

## Built on by (most cited first, 10 of 10)

- [DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning](https://mlsanity.com/paper/2111.00465/) (2021)
- [Federated Offline Reinforcement Learning](https://mlsanity.com/paper/2206.05581/) (2022)
- [Federated Multi-View Learning for Private Medical Data Integration and Analysis](https://mlsanity.com/paper/2105.01603/) (2021)
- [FedMood: Federated Learning on Mobile Health Data for Mood Detection](https://mlsanity.com/paper/2102.09342/) (2021)
- [Learning Language and Multimodal Privacy-Preserving Markers of Mood from Mobile Data](https://mlsanity.com/paper/2106.13213/) (2021)
- [Self-supervised Pretraining and Transfer Learning Enable Flu and COVID-19 Predictions in Small Mobile Sensing Datasets](https://mlsanity.com/paper/2205.13607/) (2022)
- [Personalized Federated Learning with Clustering: Non-IID Heart Rate Variability Data Application](https://mlsanity.com/paper/2108.01903/) (2021)
- [Transformer-Based Behavioral Representation Learning Enables Transfer Learning for Mobile Sensing in Small Datasets](https://mlsanity.com/paper/2107.06097/) (2021)
- [Enabling Automatic Self-Talk Detection via Earables](https://mlsanity.com/paper/2511.07493/) (2025)
- [Evaluating Federated Learning for Cross-Country Mood Inference from Smartphone Sensing Data](https://mlsanity.com/paper/2602.15478/) (2026)

## Abstract (arXiv)

The increasing use of electronic forms of communication presents new opportunities in the study of mental health, including the ability to investigate the manifestations of psychiatric diseases unobtrusively and in the setting of patients' daily lives. A pilot study to explore the possible connections between bipolar affective disorder and mobile phone usage was conducted. In this study, participants were provided a mobile phone to use as their primary phone. This phone was loaded with a custom keyboard that collected metadata consisting of keypress entry time and accelerometer movement. Individual character data with the exceptions of the backspace key and space bar were not collected due to privacy concerns. We propose an end-to-end deep architecture based on late fusion, named DeepMood, to model the multi-view metadata for the prediction of mood scores. Experimental results show that 90.31% prediction accuracy on the depression score can be achieved based on session-level mobile phone typing dynamics which is typically less than one minute. It demonstrates the feasibility of using mobile phone metadata to infer mood disturbance and severity.