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Harnessing Indigenous Tweets: The Reo Māori Twitter corpus

Te reo Māori, the Indigenous language of Aotearoa New Zealand, is a distinctive feature of the nation’s cultural heritage. This paper documents our efforts to build a corpus of 79,000 Māori-language tweets using computational methods. The Reo Māori Twitter (RMT) Corpus was created by targeting Māori...

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Autores principales: Trye, David, Keegan, Te Taka, Mato, Paora, Apperley, Mark
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Netherlands 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8852919/
https://www.ncbi.nlm.nih.gov/pubmed/35194415
http://dx.doi.org/10.1007/s10579-022-09580-w
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author Trye, David
Keegan, Te Taka
Mato, Paora
Apperley, Mark
author_facet Trye, David
Keegan, Te Taka
Mato, Paora
Apperley, Mark
author_sort Trye, David
collection PubMed
description Te reo Māori, the Indigenous language of Aotearoa New Zealand, is a distinctive feature of the nation’s cultural heritage. This paper documents our efforts to build a corpus of 79,000 Māori-language tweets using computational methods. The Reo Māori Twitter (RMT) Corpus was created by targeting Māori-language users identified by the Indigenous Tweets website, pre-processing their data and filtering out non-Māori tweets, together with other sources of noise. Our motivation for creating such a resource is three-fold: (1) it serves as a rich and unique dataset for linguistic analysis of te reo Māori on social media; (2) it can be used as training data to develop and augment Natural Language Processing (NLP) tools with robust, real-world Māori-language applications; and (3) it will potentially promote awareness of, and encourage positive interaction with, the growing community of Māori tweeters, thereby increasing the use and visibility of te reo Māori in an online environment. While the corpus captures data from 2007 to 2020, our analysis shows that the number of tweets in the RMT Corpus peaked in 2014, and the number of active tweeters peaked in 2017, although at least 600 users were still active in 2020. To the best of our knowledge, the RMT Corpus is the largest publicly-available collection of social media data containing (almost) exclusively Māori text, making it a useful resource for language experts, NLP developers and Indigenous researchers alike. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10579-022-09580-w.
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spelling pubmed-88529192022-02-18 Harnessing Indigenous Tweets: The Reo Māori Twitter corpus Trye, David Keegan, Te Taka Mato, Paora Apperley, Mark Lang Resour Eval Original Paper Te reo Māori, the Indigenous language of Aotearoa New Zealand, is a distinctive feature of the nation’s cultural heritage. This paper documents our efforts to build a corpus of 79,000 Māori-language tweets using computational methods. The Reo Māori Twitter (RMT) Corpus was created by targeting Māori-language users identified by the Indigenous Tweets website, pre-processing their data and filtering out non-Māori tweets, together with other sources of noise. Our motivation for creating such a resource is three-fold: (1) it serves as a rich and unique dataset for linguistic analysis of te reo Māori on social media; (2) it can be used as training data to develop and augment Natural Language Processing (NLP) tools with robust, real-world Māori-language applications; and (3) it will potentially promote awareness of, and encourage positive interaction with, the growing community of Māori tweeters, thereby increasing the use and visibility of te reo Māori in an online environment. While the corpus captures data from 2007 to 2020, our analysis shows that the number of tweets in the RMT Corpus peaked in 2014, and the number of active tweeters peaked in 2017, although at least 600 users were still active in 2020. To the best of our knowledge, the RMT Corpus is the largest publicly-available collection of social media data containing (almost) exclusively Māori text, making it a useful resource for language experts, NLP developers and Indigenous researchers alike. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10579-022-09580-w. Springer Netherlands 2022-02-14 2022 /pmc/articles/PMC8852919/ /pubmed/35194415 http://dx.doi.org/10.1007/s10579-022-09580-w Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Paper
Trye, David
Keegan, Te Taka
Mato, Paora
Apperley, Mark
Harnessing Indigenous Tweets: The Reo Māori Twitter corpus
title Harnessing Indigenous Tweets: The Reo Māori Twitter corpus
title_full Harnessing Indigenous Tweets: The Reo Māori Twitter corpus
title_fullStr Harnessing Indigenous Tweets: The Reo Māori Twitter corpus
title_full_unstemmed Harnessing Indigenous Tweets: The Reo Māori Twitter corpus
title_short Harnessing Indigenous Tweets: The Reo Māori Twitter corpus
title_sort harnessing indigenous tweets: the reo māori twitter corpus
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8852919/
https://www.ncbi.nlm.nih.gov/pubmed/35194415
http://dx.doi.org/10.1007/s10579-022-09580-w
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