Transitude: Machine Translation on Social Media: MT as a potential tool for opinion (mis)formation

Khetam Sharou, Joss Moorkens


Abstract
Misinformation on social media is a concern for content creators, consumers and regulators alike. Transitude looks at misinformation generated by machine translation (MT) through distortion of the intention and sentiment of text. It is the first study of MT’s impact on the formation of users’ views of society through refugees in Ireland. It extends current MT evaluation methods with a new quality evaluation framework, producing the first dataset annotated for information distortion. It provides insights into the risks of relying on MT, with recommendations for users, developers, and policymakers.
Anthology ID:
2024.eamt-2.2
Volume:
Proceedings of the 25th Annual Conference of the European Association for Machine Translation (Volume 2)
Month:
June
Year:
2024
Address:
Sheffield, UK
Editors:
Carolina Scarton, Charlotte Prescott, Chris Bayliss, Chris Oakley, Joanna Wright, Stuart Wrigley, Xingyi Song, Edward Gow-Smith, Mikel Forcada, Helena Moniz
Venue:
EAMT
SIG:
Publisher:
European Association for Machine Translation (EAMT)
Note:
Pages:
2–3
Language:
URL:
https://aclanthology.org/2024.eamt-2.2
DOI:
Bibkey:
Cite (ACL):
Khetam Sharou and Joss Moorkens. 2024. Transitude: Machine Translation on Social Media: MT as a potential tool for opinion (mis)formation. In Proceedings of the 25th Annual Conference of the European Association for Machine Translation (Volume 2), pages 2–3, Sheffield, UK. European Association for Machine Translation (EAMT).
Cite (Informal):
Transitude: Machine Translation on Social Media: MT as a potential tool for opinion (mis)formation (Sharou & Moorkens, EAMT 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.eamt-2.2.pdf