Predicting User Code-Switching Level from Sociological and Psychological Profiles

TL;DR

This paper investigates Arabic-English code-switching by analyzing sociological and psychological factors, using machine learning to predict CS frequency with over 55% accuracy, highlighting travel and personality traits as key influences.

Contribution

It provides an empirical study linking user traits to code-switching behavior and validates these correlations through machine learning models.

Findings

Travel experiences significantly influence CS frequency.

Personality traits are strong predictors of CS behavior.

ML models achieved over 55% accuracy in predicting CS levels.

Abstract

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.