FROM WORDS TO ERRORS: ANALYZING LEXICAL CHALLENGES IN INSTAGRAM'S MACHINE TRANSLATION OF 'NYTIMES’
In the current digital era, disseminating information via social media is very easy and quick, but this can also increase the spread of wrong and inaccurate information to the public caused by translation errors in automatic translations on social media. The main objectives of this research are 1....
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| Format: | Article |
| Language: | English |
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English Literature Department, Universitas Bangka Belitung
2024-12-01
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| Series: | Lire Journal |
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| Online Access: | https://jurnalsastraubb.id/index.php/elit/article/view/386 |
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| author | I Putu Riky Wiwekananda Ni Made Verayanti Utami |
| author_facet | I Putu Riky Wiwekananda Ni Made Verayanti Utami |
| author_sort | I Putu Riky Wiwekananda |
| collection | DOAJ |
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In the current digital era, disseminating information via social media is very easy and quick, but this can also increase the spread of wrong and inaccurate information to the public caused by translation errors in automatic translations on social media. The main objectives of this research are 1. Identify the types of translation errors found in Instagram Machine Translation in Translating the Caption of the "NYTimes" Instagram Account. 2. search for the most dominant types of lexical errors made by Instagram Machine Translation in Translating the Caption of "NYTimes" Instagram Account. The theory of error classification by Vilar et al. (2006) is applied to identify three main types of errors: unknown words, missing words, and incorrect words. The research method involves analyzing 100 translated news captions. There are three types of Lexical Errors found in Instagram machine translation when translating captions from the NYTimes Instagram account (1) Incorrect Word (4 instances, 50%) (2) Omitted or Missing Word (2 instances, 25% (3) Unknown Word (2 instances, 25%). The most common type of error identified in the translations is the use of incorrect words, accounting for half of all errors. This indicates that the machine translation often fails to select the appropriate words to convey the original meaning effectively.
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| format | Article |
| id | doaj-art-e0eb7dc1000142d7bca0c24eae69e9e5 |
| institution | Kabale University |
| issn | 2598-1803 2581-2130 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | English Literature Department, Universitas Bangka Belitung |
| record_format | Article |
| series | Lire Journal |
| spelling | doaj-art-e0eb7dc1000142d7bca0c24eae69e9e52025-01-10T05:22:43ZengEnglish Literature Department, Universitas Bangka BelitungLire Journal2598-18032581-21302024-12-019110.33019/lire.v9i1.386FROM WORDS TO ERRORS: ANALYZING LEXICAL CHALLENGES IN INSTAGRAM'S MACHINE TRANSLATION OF 'NYTIMES’I Putu Riky Wiwekananda0Ni Made Verayanti Utami1Universitas Mahasaraswati DenpasarUniversitas Mahasaraswati Denpasar In the current digital era, disseminating information via social media is very easy and quick, but this can also increase the spread of wrong and inaccurate information to the public caused by translation errors in automatic translations on social media. The main objectives of this research are 1. Identify the types of translation errors found in Instagram Machine Translation in Translating the Caption of the "NYTimes" Instagram Account. 2. search for the most dominant types of lexical errors made by Instagram Machine Translation in Translating the Caption of "NYTimes" Instagram Account. The theory of error classification by Vilar et al. (2006) is applied to identify three main types of errors: unknown words, missing words, and incorrect words. The research method involves analyzing 100 translated news captions. There are three types of Lexical Errors found in Instagram machine translation when translating captions from the NYTimes Instagram account (1) Incorrect Word (4 instances, 50%) (2) Omitted or Missing Word (2 instances, 25% (3) Unknown Word (2 instances, 25%). The most common type of error identified in the translations is the use of incorrect words, accounting for half of all errors. This indicates that the machine translation often fails to select the appropriate words to convey the original meaning effectively. https://jurnalsastraubb.id/index.php/elit/article/view/386lexical errorsautomatic translationsocial media |
| spellingShingle | I Putu Riky Wiwekananda Ni Made Verayanti Utami FROM WORDS TO ERRORS: ANALYZING LEXICAL CHALLENGES IN INSTAGRAM'S MACHINE TRANSLATION OF 'NYTIMES’ Lire Journal lexical errors automatic translation social media |
| title | FROM WORDS TO ERRORS: ANALYZING LEXICAL CHALLENGES IN INSTAGRAM'S MACHINE TRANSLATION OF 'NYTIMES’ |
| title_full | FROM WORDS TO ERRORS: ANALYZING LEXICAL CHALLENGES IN INSTAGRAM'S MACHINE TRANSLATION OF 'NYTIMES’ |
| title_fullStr | FROM WORDS TO ERRORS: ANALYZING LEXICAL CHALLENGES IN INSTAGRAM'S MACHINE TRANSLATION OF 'NYTIMES’ |
| title_full_unstemmed | FROM WORDS TO ERRORS: ANALYZING LEXICAL CHALLENGES IN INSTAGRAM'S MACHINE TRANSLATION OF 'NYTIMES’ |
| title_short | FROM WORDS TO ERRORS: ANALYZING LEXICAL CHALLENGES IN INSTAGRAM'S MACHINE TRANSLATION OF 'NYTIMES’ |
| title_sort | from words to errors analyzing lexical challenges in instagram s machine translation of nytimes |
| topic | lexical errors automatic translation social media |
| url | https://jurnalsastraubb.id/index.php/elit/article/view/386 |
| work_keys_str_mv | AT iputurikywiwekananda fromwordstoerrorsanalyzinglexicalchallengesininstagramsmachinetranslationofnytimes AT nimadeverayantiutami fromwordstoerrorsanalyzinglexicalchallengesininstagramsmachinetranslationofnytimes |