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.

借助迁移相关的社交媒体平台上的代码混合这一多语言演讲方式，我们提出了 “多语言代码混合文本的整体学习识别”(ELMICT) 的新方法，通过结合多个标记化器输出和预训练语言模型的集成学习技术，ELMICT 在识别各种语言和语境下的代码混合方面表现出高性能 (准确率 F1 大于 0.95)，特别是在跨语言零样本条件下 (平均准确率 F1 大于 0.70)。此外，利用 ELMICT 还可以分析与其他主题类别相比，迁移相关的线图上的代码混合的普遍性，从而揭示出移民社区关注的话题。我们的研究结果揭示了移民在社交媒体平台上采用的沟通策略，为开发包容性数字公共服务和对话系统提供了有益的启示。通过解决本研究提出的研究问题，我们有助于理解移民语言多样性，并为建设多元文化社会中建立信任的更有效工具铺平了道路。