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

arXiv:2404.16066v1 Announce Type: cross
Abstract: 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 …

abstract app arxiv behavior cs.hc cs.lg cs.si habits literature lstm media modeling networks neural networks novel paper predictive predictive modeling report simple smartphone social social media studying technology through transformer type while