# The Who in Code-Switching: A Case Study for Predicting Egyptian Arabic-English Code-Switching Levels based on Character Profiles

arXiv: https://arxiv.org/abs/2208.00433 · HTML: https://mlsanity.com/paper/2208.00433/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 76 references that are in the map.
- First posted on arXiv 31 Jul 2022.

## Builds on (most cited first, 11 of 11)

- [A Survey of Code-switched Speech and Language Processing](https://mlsanity.com/paper/1904.00784/) (2019)
- [Towards One Model to Rule All: Multilingual Strategy for Dialectal Code-Switching Arabic ASR](https://mlsanity.com/paper/2105.14779/) (2021)
- [Challenges of Computational Processing of Code-Switching](https://mlsanity.com/paper/1610.02213/) (2016)
- [Arabic Code-Switching Speech Recognition using Monolingual Data](https://mlsanity.com/paper/2107.01573/) (2021)
- [Exploring Text-to-Text Transformers for English to Hinglish Machine Translation with Synthetic Code-Mixing](https://mlsanity.com/paper/2105.08807/) (2021)
- [End-to-End Code-Switching ASR for Low-Resourced Language Pairs](https://mlsanity.com/paper/1909.12681/) (2019)
- [Investigations on Speech Recognition Systems for Low-Resource Dialectal Arabic-English Code-Switching Speech](https://mlsanity.com/paper/2108.12881/) (2021)
- [Investigating Lexical Replacements for Arabic-English Code-Switched Data Augmentation](https://mlsanity.com/paper/2205.12649/) (2022)
- [Code-switching Language Modeling With Bilingual Word Embeddings: A Case Study for Egyptian Arabic-English](https://mlsanity.com/paper/1909.10892/) (2019)
- [Integrating Knowledge in End-to-End Automatic Speech Recognition for Mandarin-English Code-Switching](https://mlsanity.com/paper/2112.10202/) (2021)
- [Predicting User Code-Switching Level from Sociological and Psychological Profiles](https://mlsanity.com/paper/2112.06462/) (2021)

## Built on by (most cited first, 1 of 1)

- [A Survey of Code-switched Arabic NLP: Progress, Challenges, and Future Directions](https://mlsanity.com/paper/2501.13419/) (2025)

## Abstract (arXiv)

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 users' CS levels and character traits. We conduct interviews with bilinguals and gather information on their profiles, including their demographics, personality traits, and traveling experiences. We then use machine learning (ML) to predict users' CS levels based on their profiles, where we identify the main influential factors in the modeling process. We experiment with both classification as well as regression tasks. Our results show that the CS behaviour is affected by the relation between speakers, travel experiences as well as Neuroticism and Extraversion personality traits.