Measuring the Technique and Quality of Instagram Caption Auto-Translation to Improve @alarabiya Account Translation Results - Gelsya Serinastity; M. Zacky Mubarok; Akhmad Saehudin; Darsita Suparno; Fathalah Mohamed Fathalah (Universitas Islam Negeri Palangka Raya, 2025)
The increasing use of machine translation (MT) on social media poses challenges for cross-linguistic and cross-cultural understanding, as these systems often fail to capture contextual meaning and cultural nuances. This study analyzes the techniques and quality of MT in social media content, an area with limited prior research. Using a qualitative-descriptive method, the study evaluates captions from Instagram @alarabiya by applying Molina & Albir's (2002) translation technique theory and Nababan's (2012) translation quality assessment scale. Analysis of 18 data samples revealed the dominance of the literal translation technique (27.27%). The quality assessment yielded average scores below the 'adequate' category in readability (μ=2.24), acceptability (μ=2.43), and accuracy (μ=2.50) on a 1-5 scale. These findings confirm the limitations of MT in processing informative texts rich in cultural context, resulting in outputs that tend to be difficult to understand, less acceptable, and inaccurate. This study highlights the urgency of enhancing cultural sensitivity in MT development and the importance of user literacy in critically engaging with automated translation outputs on digital platforms.