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.

通过对连续智能手机用户行为的预测建模，本文引入了一种研究社交媒体习惯的新方法。使用长短期记忆（LSTM）和变压器神经网络，我们展示了社交媒体使用在个人内部和个人之间是可预测的，同时在个体之间的预测差异也是稳健的。