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Code-Mixing in Tanzanian Telecommunication Advertisements: A Matrix Language Frame Analysis of Kiswahili, Kiswahili cha Mtaani, and English

Domaine:

natural language processing

Type de record:

paper
Créateur:
FelJosHad
Éditeur:
The
Hôte:
Although code-mixing has traditionally been discouraged in structured communication (including publications and mass media), telecommunications advertisements increasingly use it to engage diverse audiences, particularly younger consumers, and remain competitive. Meanwhile, previous research has predominantly focused on codemixing in everyday conversations and classroom settings, leaving code-mixing in contemporary media underexplored. Consequently, the present study examined the structural forms of code-mixing in telecommunication advertisements in Tanzania, focusing on the interaction among Standard Kiswahili, Kiswahili cha Mtaani (KcM), and English. The study adopted a qualitative approach and purposive sampling to select audiovisual advertisements from major telecommunication companies on social media platforms in Tanzania. Data were analysed qualitatively using Carol Myers-Scotton's (1993, 2006) Matrix Language Frame (MLF) model. The findings reveal four recurrent structural forms of code-mixing: lexical borrowing, intra-word mixing, register mixing, and transitional (nonce) forms, with Kiswahili consistently functioning as the matrix language. English and Kiswahili cha Mtaani provide embedded lexical elements associated with technology, modernity, efficiency, and urban identity. Transitional forms such as strimu, cheki, spidi, and supa exhibit partial phonological and morphological adaptation to Kiswahili, suggesting a continuum between code-switching and borrowing. The study concludes that code-mixing in telecommunication advertising is a systematic and strategically deployed communicative resource that enhances audience engagement while projecting technological modernity and urban identity. Further research may examine audience perceptions of code-mixed advertising across different demographic groups.