  Multilingual speakers tend to alternate between languages within a
conversation, a phenomenon referred to as "code-switching" (CS). CS is a
complex phenomenon that not only encompasses linguistic challenges, but also
contains a great deal of complexity in terms of its dynamic behaviour across
speakers. This dynamic behaviour has been studied by sociologists and
psychologists, identifying factors affecting CS. In this paper, we provide an
empirical user study on Arabic-English CS, where we show the correlation
between users' CS frequency and character traits. We use machine learning (ML)
to validate the findings, informing and confirming existing theories. The
predictive models were able to predict users' CS frequency with an accuracy
higher than 55%, where travel experiences and personality traits played the
biggest role in the modeling process.
