A Comparative Analysis of Human and Machine Translation: A Study of Meaning Loss in English–Urdu Social Media Posts

Abstract

Machine Translation (MT) techniques are extensively used to translate English social media postings into Urdu, however they frequently fail to capture the original text's full meaning, emotional tone, and cultural expression.  Although machine translation (MT) has become commonplace in everyday communication, little study has compared MT output to human translation to uncover meaning loss in social media discourse.Using Nida's Dynamic Equivalence theory, this study compares human and machine translations of English social media posts to see how meaning changes or diminishes during MT. The data indicate that machine translation typically creates literal, artificial, and emotionally flat Urdu versions, resulting in the loss of inferred meaning, cultural nuance, and expressive tone.  Human translations, on the other hand, were more contextually relevant and accurate.  The study suggests that machine translation is unreliable for translating emotionally rich or informal social media content, emphasizing the importance of human engagement in preserving meaning more correctly.