Audiovisual translation (AVT) is crucial given the vast amount of audiovisual material available. This study focuses on subtitling, a widely researched AVT mode, examining the translation of English swear words into Arabic on Netflix. Swear words, expressing impolite sentiments and reactions, present unique challenges in translation due to cultural and linguistic differences. The study aims to identify the most common translation strategies employed by professional Netflix subtitlers when rendering English swear words into Arabic and to analyze how these strategies vary across different movie genres (drama, action, sci-fi, and biography). This research combines corpus linguistics and translation studies, utilizing SketchEngine software to analyze a large parallel corpus of English-Arabic movie subtitles. The study is both qualitative and quantitative, providing both frequency counts of translation strategies and detailed examples of the strategies used in context.

Existing research on subtitling highlights the distinction between professional subtitling and fansubbing, with professionals prioritizing efficiency and commercial viability, while fansubbers may prioritize authenticity for a niche audience. The definition and classification of swear words are debated, varying based on linguistic, social, and psychological functions. Swear words are multifunctional, carrying denotative and connotative meanings; the latter are culturally dependent. The translation of swear words into Arabic is particularly challenging due to the cultural conservatism of Arab societies and the contrast between Modern Standard Arabic (MSA), used in subtitling, and colloquial Arabic vernaculars. Previous research on taboo word translation into Arabic has often identified omission and euphemism as primary strategies, but there is a lack of studies examining large corpora of Netflix subtitles. This study aims to fill this gap by investigating the strategies used by professional subtitlers on a large scale.

This study employed a quantitative and qualitative approach using a 699,229-word English-Arabic parallel corpus extracted from the English-Arabic Movie Subtitles Corpus (EAMSC), comprising subtitles of 40 Netflix movies (10 each from drama, action, sci-fi, and biography genres) with high IMDB ratings (+8). Data extraction involved using Aegisub to align English transcriptions with Arabic subtitles, then transferring the data to an Excel sheet for segmentation and alignment. SketchEngine, a web-based corpus analysis tool, was used for frequency analysis and parallel concordance to identify the ten most frequent English swear words and their Arabic translations. The translation strategies were categorized into three groups based on Khoshsaligheh and Ameri's (2014) model: taboo-to-taboo, taboo-to-non-taboo, euphemism/softening, and omission. The study analyzed the frequency of each strategy for each swear word within each genre and examined how the offensiveness of swear words and genre influenced the choice of translation strategy.

The ten most frequent swear words in the corpus were: fuck, shit, damn, ass, bitch, bastard, asshole, dick, cunt, and pussy. The total number of swear words analyzed was 1564. Omission was the most common strategy across all genres, ranging from 40% (Sci-Fi) to 66% (Drama). Softening was the second most common strategy (20–39%), and swear-to-non-swear was the least used (11–21%). Drama movies had the highest frequency of swear words overall (61%), followed by action and sci-fi (14% each), and biography (11%). The choice of strategy varied depending on the specific swear word and its perceived level of offensiveness. Stronger swear words like "cunt" and "fuck" were more frequently omitted, while less offensive words were more often softened or replaced with non-swear words. The study also analyzed the most frequent Arabic equivalents for each English swear word, noting the use of euphemisms and milder terms in MSA to adhere to cultural norms.

The high frequency of omission reflects the cultural constraints and censorship practices within the Arab context. The preference for MSA in subtitling, which is generally more formal and polite than colloquial Arabic, further contributes to the mitigation of offensiveness. The variation in strategy across genres supports the notion that the context of the swear word influences the translator's choice. The use of euphemism and less offensive replacements demonstrates a balance between maintaining the semantic meaning and adhering to cultural sensitivities. The findings underscore the complex interplay between linguistic choices, cultural norms, and technical constraints in audiovisual translation.

This study provides a comprehensive analysis of swear word translation strategies in Netflix English-Arabic subtitles, using a large corpus. The prevalence of omission highlights the significant influence of cultural norms and censorship in Arab media. Future research could explore fansubbing practices and other AVT modes (dubbing, voiceover), expand to other genres and languages, and delve deeper into the pragmatic and connotative functions of swear words. These findings offer valuable insights for subtitlers, translation students, and training programs.

The study's data was limited to Netflix subtitles, potentially excluding variations found in fansubbing or other platforms. The focus on four genres may not fully represent the spectrum of swear word usage across all film types. Further research could explore the potential impact of subjective interpretations of offensiveness on translation choices.