THERESA MAY’S AND JEREMY CORBYN’S SPOKEN LANGUAGE DIFFERENCES IN THE UNITED KINGDOM ELECTION DEBATE 2017

FRANSISKA DIANITA VIORENTINA, 121411233010 (2018) THERESA MAY’S AND JEREMY CORBYN’S SPOKEN LANGUAGE DIFFERENCES IN THE UNITED KINGDOM ELECTION DEBATE 2017. Skripsi thesis, Universitas Airlangga.

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Abstract

Spoken language and gender differences tend to create different language usages in how men and women using language. In analyzing language and gender, one of the ways to distinguish is by applying spoken differences. Spoken differences explain how men and women differences in using language proposed by Haas (1979). This research analyzed the spoken differences of language and gender used by Theresa May and Jeremy Corbyn as the candidates in the United Kingdom Election Debate 2017. According to Haas (1979), spoken differences used by men and women in terms of form, topic, content, and use were applied to analyze the language and gender in the debate. This research proves that male and female candidates showed the spoken differences during the debate. The dominance of men in politics has an effect on how women use the language. As a result, woman as politicians use men’s language in order to grab attention from the audience. Therefore, the similarities of spoken differences used by male and female candidates are also found during the debate. Data were taken from the video titled May vs Corbyn: The Battle for Number 10 – The Full Programme uploaded by Sky News on YouTube. Qualitative approach was used as the research method in this study. In addition, the results show spoken differences in using language between male and female candidates in the debate.

Item Type: Thesis (Skripsi)
Additional Information: KKB KK2 FS BE 150/18 Vio t
Uncontrolled Keywords: DEBATE; GENDER; POLITICS; SPOKEN DIFFERENCES
Subjects: P Language and Literature
P Language and Literature > PE English > PE1-3729 English
Divisions: 12. Fakultas Ilmu Budaya > Sastra Inggris
Creators:
CreatorsNIM
FRANSISKA DIANITA VIORENTINA, 121411233010UNSPECIFIED
Contributors:
ContributionNameNIDN / NIDK
Thesis advisorERLITA RUSNARINGTIAS, M.A.UNSPECIFIED
Depositing User: Ani Sistarina
Date Deposited: 22 Oct 2018 09:51
Last Modified: 22 Oct 2018 09:51
URI: http://repository.unair.ac.id/id/eprint/74975
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