Strategies of translating swear words into Arabic: a case study of a parallel corpus of Netflix English-Arabic movie subtitles

Abstract

AbstractThis study adopts a corpus-assisted approach to explore the translation strategies that Netflix subtitlers opted for in rendering 1564 English swear words into Arabic. It uses a 699,229-word English-Arabic parallel corpus consisting of the English transcriptions of forty English movies, drama, action, science fiction (sci-fi), and biography and their Arabic subtitles. Using the wordlist tool in SketchEngine, the researchers identified some frequent swear words, namelyfuck, shit, damn, ass, bitch, bastard, asshole, dick, cunt, andpussy. Moreover, using the parallel concordance tool in SketchEngine revealed that three translation strategies were observed in the corpus, namely, omission, softening, and swear-to-non-swear. The omission strategy accounted for the lion’s share in the investigated data, with 66% for drama, 61% for action, 52% for biography, and 40% for sci-fi. On the other hand, the swear-to-non-swear strategy was the least adopted one, accounting for 21% in sci-fi, 16% in biography, 14% in drama, and 11% in action. In addition, the softening strategy got the second-highest frequency across the different movie genres, with 39% for sci-fi, 32% for biography, 28% for action, and 20% for drama. Since swear words have connotative functions, omitting or euphemizing them could cause a slight change in the representation of meaning and characters. The study recommends more corpus-assisted studies on different AVT modes, including dubbing, voiceover, and free commentaries.