Subtitling strategies of swear words in the stand-up comedy Mo Amer: Muhammad in Texas.
Published In: Babel: International Journal of Translation / Revue Internationale de la Traduction / Revista Internacional de Traducción, 2025, v. 71, n. 1. P. 81 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Sawi, Islam Al 3 of 3
Abstract
Stand-up comedies often employ swear words as a technique to create audience rapport and playful discourse. However, translators face significant challenges in subtitling swear words in these performances for conservative cultures, such as Arabic. This research uses a qualitative and quantitative approach to analyze the Netflix special Mo Amer: Muhammad in Texas to identify swear words, their Arabic subtitles, and the subtitling strategies used and their frequency, utilizing Ljung's (2011) swear words' classification and Khoshsaligheh and Ameri's (2014) subtitling framework. The results revealed that among the 174 identified swear words, "fuck" and "shit" were the most frequently used, at 52% and 16% respectively. Translators employed euphemism, deletion, and taboo to non-taboo strategies, with euphemism emerging as the most predominant at 44%. The strategy of subtitling via taboo to taboo was not used when rendering swear words into Arabic, probably due to cultural considerations for the audience. The findings enhance cross-cultural subtitling practices for stand-up comedy and promote inclusive and engaging experiences for diverse audiences. Further implications are discussed. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Babel: International Journal of Translation / Revue Internationale de la Traduction / Revista Internacional de Traducción. 2025/01, Vol. 71, Issue 1, p81
- Document Type:Article
- Subject Area:Computer Science
- Publication Date:2025
- ISSN:0521-9744
- DOI:10.1075/babel.00401.saw
- Accession Number:182796705
- Copyright Statement:Copyright of Babel: International Journal of Translation / Revue Internationale de la Traduction / Revista Internacional de Traducción is the property of John Benjamins Publishing Co. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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