TRANSLATION CHALLENGES AND POST-EDITING STRATEGIES OF AUTOMATICALLY TRANSLATED SUBTITLES IN YOUTUBE FOOD VLOGS

Abstract

This study explores the linguistic and cultural challenges encountered in automatically translated subtitles of YouTube food vlogs and proposes effective post-editing strategies. The research focuses on three popular channels — Refika’s Kitchen, Nino’s Home, and Cooking with Aisha — which represent diverse linguistic and cultural contexts. The analysis follows a qualitative descriptive method comparing raw machine translations with human-edited versions to identify lexical, idiomatic, and syntactic inconsistencies. Results reveal that most translation errors arise from domain-specific terminology, idiomatic expressions, and culturally embedded concepts that automated systems fail to interpret correctly. To address these issues, practical post-editing strategies are introduced, including specialized glossaries, contextual rewriting, cultural localization, and human-in-the-loop workflows. Implementing these methods reduced major translation errors by 68%, improving subtitle clarity and cultural relevance. The study concludes that although machine translation accelerates subtitling, it still depends on human expertise for semantic accuracy and intercultural authenticity in digital media.