Translation Techniques and Translation Quality on Political News Headlines Translation in Cable News Network (CNN)

Artiana, Nisa (2025) Translation Techniques and Translation Quality on Political News Headlines Translation in Cable News Network (CNN). Undergraduate (S1) thesis, IAIN Kediri.

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Abstract

Translating news headlines is one of the most challenging tasks for media translators. The main challenge lies in choosing the appropriate translation technique to ensure that the message is effectively delivered in the target language. In the era of globalization and easy access to information, headlines are the first element that captures readers' attention and plays a crucial role in cross-border information exchange. Therefore, this study aims to identify the translation techniques used in CNN’s political news headlines, determine the most frequently used technique, and evaluate the translation quality in terms of accuracy, acceptability, and readability. This research employs a descriptive qualitative method, using the translation technique framework by Molina and Albir (2002) and the translation quality assessment model by Nababan (2012). The data consist of 50 pairs of English and Indonesian political news headlines, specifically focused on the Israel–Hamas, sourced from CNN's official websites (cnn.com for English and cnnindonesia.com for the Indonesian language). The identified techniques were then analyzed based on their frequency of use and the quality of their translations. The findings reveal that 16 different translation techniques were used in the translation of CNN’s political news headlines. The most frequently used technique is Reduction with 32 instances, followed by Modulation with 28 instances, and Linguistic Compression with 20 instances. In terms of quality, the majority of the translations received high scores for accuracy, acceptability, and readability. These results indicate that CNN’s political news headline translations generally meet the standards of good translation quality.

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