Social Media Use is Predictable from App Sequences: Using LSTM and Transformer Neural Networks to Model Habitual Behavior
<|reference_start|>Social Media Use is Predictable from App Sequences: Using LSTM and Transformer Neural Networks to Model Habitual Behavior: 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.<|reference_end|>
@article{peters2024social,
    title={Social Media Use is Predictable from App Sequences: Using LSTM and
  Transformer Neural Networks to Model Habitual Behavior},
    author={Heinrich Peters, Joseph B. Bayer, Sandra C. Matz, Yikun Chi, Sumer S.
  Vaid, and Gabriella M. Harari},
    journal={arXiv preprint arXiv:2404.16066},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.16066},
    primaryClass={cs.HC cs.LG cs.SI}
}