Presupposition in code-mixing utterance by Twitter user

Hasya, Dhiya'un Nabila (2022) Presupposition in code-mixing utterance by Twitter user. Sarjana thesis, UIN Sunan Gunung Djati Bandung.

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Abstract

Presupposition is the result of the thinking process in categorizing the meaning in the speaker’s utterance, it's also important for understanding the meaning because it incorporates predictions that fit the context and makes presupposition important for understanding the meaning of an utterance. Therefore, research on this topic is necessary and interesting to do with research questions: 1. what types of presuppositions are used in code-mixing utterances of Twitter users? 2. How do the presuppositions generate meanings in the code-mixing utterances of Twitter users? This research used qualitative research methods and the main theory is from Yule (1996). Based on data analysis, the findings showed 10 for existential presuppositions which was an expression of ownership of something. 6 for lexical presuppositions which expressed an unspoken concept using the suffix “-ed” (5 data) and using the word “again” is 1 datum as the trigger, 6 for structural presuppositions which referred to the assumptions involved in an utterance using words containing asking sentences (3 data) and using WH-questions are 3 data as the trigger, 15 for factive presuppositions that expressed facts using the words “realize” are 12 data, “know” are 2 data, and “glad” is 1 datum as the trigger, 6 for counter-factual presuppositions with the meaning based on the speaker's situation when expressing his utterance are 2 data, the meaning based on utterances was just an examples of events are 2 data, the meaning expressing a fact that was still being debated is 1 datum, the meaning was based on a situation before is 1 datum, 7 for non-factive presuppositions which was the assumption refered to something that was not a fact. From the findings of data analysis, it is concluded that these factive presupposition was the most commonly used in the utterances code-mixing by Twitter users which indicates the fact in the utterances.

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