The Who in Code-Switching: A Case Study for Predicting Egyptian Arabic-English Code-Switching Levels based on Character Profiles
Injy Hamed, Alia El Bolock, Cornelia Herbert, Slim Abdennadher, Ngoc, Thang Vu

TL;DR
This study investigates how sociological and psychological factors influence code-switching levels among Egyptian Arabic-English bilinguals, using machine learning to predict CS based on user profiles and identifying key influencing traits.
Contribution
It introduces an empirical approach combining user interviews and machine learning to predict code-switching behavior from sociological and psychological data.
Findings
Travel experiences influence CS levels
Neuroticism and Extraversion traits affect CS behavior
ML models can predict CS levels from user profiles
Abstract
Code-switching (CS) is a common linguistic phenomenon exhibited by multilingual individuals, where they tend to alternate between languages within one single conversation. 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. Given that the factors giving rise to CS vary from one country to the other, as well as from one person to the other, CS is found to be a speaker-dependant behaviour, where the frequency by which the foreign language is embedded differs across speakers. While several researchers have looked into predicting CS behaviour from a linguistic point of view, research is still lacking in the task of predicting user CS behaviour from sociological and psychological perspectives. We provide an empirical user study, where we investigate the correlations between…
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Taxonomy
TopicsMultilingual Education and Policy · Digital Communication and Language · Language, Discourse, Communication Strategies
MethodsEmirates Airlines Office in Dubai
