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Machine Translation in the Wild: User Reaction to Xiaohongshu's Built-In Translation Feature

Sui He

Abstract

The growing integration of machine translation into social media platforms is transforming how users interact with each other across cultural and linguistic boundaries. This paper examines user reactions to the launch of Xiaohongshu's built-in translation feature in January 2025. Drawing on a dataset of 6,723 comments collected from 11 official posts promoting the translation function, this paper combines sentiment analysis with thematic analysis to investigate how users perceived and experimented with the function. Results show that reactions were generally positive, particularly for translating posts and comments, although concerns regarding functionality, accessibility, and translation accuracy were also expressed. In addition to evaluative feedback, users actively tested the function with diverse inputs, including words and phrases in English and Chinese, abbreviations in pinyin, internet slang, and other language forms such as emoji, kaomoji, coded texts, etc. The findings highlight the importance of closer collaboration among computer scientists, translation scholars, and platform designers to better understand and improve translation technologies in real world communicative context.

Machine Translation in the Wild: User Reaction to Xiaohongshu's Built-In Translation Feature

Abstract

The growing integration of machine translation into social media platforms is transforming how users interact with each other across cultural and linguistic boundaries. This paper examines user reactions to the launch of Xiaohongshu's built-in translation feature in January 2025. Drawing on a dataset of 6,723 comments collected from 11 official posts promoting the translation function, this paper combines sentiment analysis with thematic analysis to investigate how users perceived and experimented with the function. Results show that reactions were generally positive, particularly for translating posts and comments, although concerns regarding functionality, accessibility, and translation accuracy were also expressed. In addition to evaluative feedback, users actively tested the function with diverse inputs, including words and phrases in English and Chinese, abbreviations in pinyin, internet slang, and other language forms such as emoji, kaomoji, coded texts, etc. The findings highlight the importance of closer collaboration among computer scientists, translation scholars, and platform designers to better understand and improve translation technologies in real world communicative context.
Paper Structure (19 sections, 4 figures, 1 table)

This paper contains 19 sections, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Methodology
  4. Data Collection
  5. Analytical Framework
  6. Data Cleaning
  7. Sentiment Analysis
  8. Prompt Design
  9. Thematic Analysis
  10. Results
  11. Sentiments
  12. Test Segments
  13. Discussion
  14. Perceived Performance Across Use Cases
  15. Evaluation of Translation Function
  16. ...and 4 more sections

Figures (4)

  • Figure 1: Engagement Indicators for Posts
  • Figure 2: Sentiment Proportion by Use Case
  • Figure 3: Thematic Analysis of Sentiments
  • Figure 4: Thematic Analysis of Test Segments