# Social Media Use is Predictable from App Sequences: Using LSTM and Transformer Neural Networks to Model Habitual Behavior

arXiv: https://arxiv.org/abs/2404.16066 · HTML: https://mlsanity.com/paper/2404.16066/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 99 references that are in the map.
- First posted on arXiv 20 Apr 2024.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [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)
- [BEHRT: Transformer for Electronic Health Records](https://mlsanity.com/paper/1907.09538/) (2019)
- [ESE: Efficient Speech Recognition Engine with Sparse LSTM on FPGA](https://mlsanity.com/paper/1612.00694/) (2016)
- [Using Sequences of Life-events to Predict Human Lives](https://mlsanity.com/paper/2306.03009/) (2023)
- [Characterizing and Forecasting User Engagement with In-app Action Graph: A Case Study of Snapchat](https://mlsanity.com/paper/1906.00355/) (2019)
- [Context-Aware Prediction of User Engagement on Online Social Platforms](https://mlsanity.com/paper/2310.14533/) (2023)

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

- [PhoneCLI: From App Interfaces to Callable Commands for Mobile Agents](https://mlsanity.com/paper/2609.35671/) (2026)

## Abstract (arXiv)

The present paper introduces a novel approach to studying social media habits through predictive modeling of sequential smartphone user behaviors. While much of the literature on media and technology habits has relied on self-report questionnaires and simple behavioral frequency measures, we examine an important yet understudied aspect of media and technology habits: their embeddedness in repetitive behavioral sequences. Leveraging Long Short-Term Memory (LSTM) and transformer neural networks, we show that (i) social media use is predictable at the within and between-person level and that (ii) there are robust individual differences in the predictability of social media use. We examine the performance of several modeling approaches, including (i) global models trained on the pooled data from all participants, (ii) idiographic person-specific models, and (iii) global models fine-tuned on person-specific data. Neither person-specific modeling nor fine-tuning on person-specific data substantially outperformed the global models, indicating that the global models were able to represent a variety of idiosyncratic behavioral patterns. Additionally, our analyses reveal that the person-level predictability of social media use is not substantially related to the frequency of smartphone use in general or the frequency of social media use, indicating that our approach captures an aspect of habits that is distinct from behavioral frequency. Implications for habit modeling and theoretical development are discussed.