# Predicting User Code-Switching Level from Sociological and Psychological Profiles

arXiv: https://arxiv.org/abs/2112.06462 · HTML: https://mlsanity.com/paper/2112.06462/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 48 references that are in the map.
- First posted on arXiv 13 Dec 2021.

## Builds on (most cited first, 5 of 5)

- [A Survey of Code-switched Speech and Language Processing](https://mlsanity.com/paper/1904.00784/) (2019)
- [Challenges of Computational Processing of Code-Switching](https://mlsanity.com/paper/1610.02213/) (2016)
- [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)
- [Code-switching Language Modeling With Bilingual Word Embeddings: A Case Study for Egyptian Arabic-English](https://mlsanity.com/paper/1909.10892/) (2019)

## Built on by (most cited first, 2 of 2)

- [A Survey of Code-switched Arabic NLP: Progress, Challenges, and Future Directions](https://mlsanity.com/paper/2501.13419/) (2025)
- [The Who in Code-Switching: A Case Study for Predicting Egyptian Arabic-English Code-Switching Levels based on Character Profiles](https://mlsanity.com/paper/2208.00433/) (2022)

## Abstract (arXiv)

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.