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

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