@inproceedings{sharou-moorkens-2024-transitude,
title = "Transitude: Machine Translation on Social Media: {MT} as a potential tool for opinion (mis)formation",
author = "Sharou, Khetam and
Moorkens, Joss",
editor = "Scarton, Carolina and
Prescott, Charlotte and
Bayliss, Chris and
Oakley, Chris and
Wright, Joanna and
Wrigley, Stuart and
Song, Xingyi and
Gow-Smith, Edward and
Forcada, Mikel and
Moniz, Helena",
booktitle = "Proceedings of the 25th Annual Conference of the European Association for Machine Translation (Volume 2)",
month = jun,
year = "2024",
address = "Sheffield, UK",
publisher = "European Association for Machine Translation (EAMT)",
url = "https://aclanthology.org/2024.eamt-2.2",
pages = "2--3",
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.",
}
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%0 Conference Proceedings
%T Transitude: Machine Translation on Social Media: MT as a potential tool for opinion (mis)formation
%A Sharou, Khetam
%A Moorkens, Joss
%Y Scarton, Carolina
%Y Prescott, Charlotte
%Y Bayliss, Chris
%Y Oakley, Chris
%Y Wright, Joanna
%Y Wrigley, Stuart
%Y Song, Xingyi
%Y Gow-Smith, Edward
%Y Forcada, Mikel
%Y Moniz, Helena
%S Proceedings of the 25th Annual Conference of the European Association for Machine Translation (Volume 2)
%D 2024
%8 June
%I European Association for Machine Translation (EAMT)
%C Sheffield, UK
%F sharou-moorkens-2024-transitude
%X 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.
%U https://aclanthology.org/2024.eamt-2.2
%P 2-3
Markdown (Informal)
[Transitude: Machine Translation on Social Media: MT as a potential tool for opinion (mis)formation](https://aclanthology.org/2024.eamt-2.2) (Sharou & Moorkens, EAMT 2024)
ACL