AN ANALYSIS OF USING CODE MIXING BY AURELIA VIZAL ON X APPS

Yogi, Jhowandi and Nova, Rina (2024) AN ANALYSIS OF USING CODE MIXING BY AURELIA VIZAL ON X APPS. Diploma thesis, UNIVERSITAS BUNG HATTA.

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Abstract

Currently, code-mixing is not limited to direct speech but is also observed in indirect speech, such as in social media platform X. This study examines Aurelia Vizal's utilization of code-mixing on X in a selection of her posts. The objective of this study is to identify the various forms of code-mixing and investigate the underlying factors that contribute to code-mixing. The data were examined using McKay and Nancy (1996) code-mixing and Hoffman's (1991) theory to identify the reasons for code-mixing. This research employs a qualitative methodology. McKay and Nancy (1996)) have categorized code mixing into three types: insertion of word, insertion of phrase, and insertion of clause or sentence. The writer's findings indicate that the most frequent instances of code-mixing are observed in the form of the insertion of words and insertion of clauses or sentences twenty (20) in total, followed by the insertion of phrases found in twelve (12) data. This study additionally identifies seven factors that contribute to the utilization of code-mixing in Aurelia Vizal's posts as proposed by Hoffman (1991). The identified reasons for code-mixing in this study encompass Talking about a particular topic, Quoting somebody else, Being emphatic about something, Interjection, Repetition used for Clarification, Expressing Group Identity, and Clarifying the speech content for the interlocutor. There is a total of twenty-seven (27) data points to talking about a particular topic. Two (2) of these data points are repetitions used for clarification. There are a total of eight (8) data points that consist of interjections, which are words or phrases used as sentence fillers or connectors. No data points are present in the category of clarifying speech content for the interlocutor, seven (7) data points demonstrate Being emphatic about something and quoting somebody else six (6) in total, and two (2) data points that express group identity or solidarity.

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