  The surge in global migration patterns underscores the imperative of
integrating migrants seamlessly into host communities, necessitating inclusive
and trustworthy public services. Despite the Nordic countries' robust public
sector infrastructure, recent immigrants often encounter barriers to accessing
these services, exacerbating social disparities and eroding trust. Addressing
digital inequalities and linguistic diversity is paramount in this endeavor.
This paper explores the utilization of code-mixing, a communication strategy
prevalent among multilingual speakers, in migration-related discourse on social
media platforms such as Reddit. We present Ensemble Learning for Multilingual
Identification of Code-mixed Texts (ELMICT), a novel approach designed to
automatically detect code-mixed messages in migration-related discussions.
Leveraging ensemble learning techniques for combining multiple tokenizers'
outputs and pre-trained language models, ELMICT demonstrates high performance
(with F1 more than 0.95) in identifying code-mixing across various languages
and contexts, particularly in cross-lingual zero-shot conditions (with avg. F1
more than 0.70). Moreover, the utilization of ELMICT helps to analyze the
prevalence of code-mixing in migration-related threads compared to other
thematic categories on Reddit, shedding light on the topics of concern to
migrant communities. Our findings reveal insights into the communicative
strategies employed by migrants on social media platforms, offering
implications for the development of inclusive digital public services and
conversational systems. By addressing the research questions posed in this
study, we contribute to the understanding of linguistic diversity in migration
discourse and pave the way for more effective tools for building trust in
multicultural societies.
