A bi-annotated Malay-English code-switching (Manglish) dataset of X posts for biological gender identification and authorship attribution
Low-resource languages, like Malay, face the threat of extinction when linguistic resources become scarce. This paper addresses the scarcity issue by contributing to the inventory of low-resource languages, specifically focusing on Malay-English, known as Manglish. Manglish speakers are primarily...
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| Format: | Article |
| Language: | en |
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Elsevier
2024
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| Online Access: | http://eprints.uthm.edu.my/11791/1/J17377_a3b15f369ba6e61ca5517eaf40899173.pdf http://eprints.uthm.edu.my/11791/ https://doi.org/10.1016/j.dib.2024.110034 |
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| author | Maskat, Ruhaila Azman, Norazmiera Ayunie Nulizairos, Nur Shaheera Shastera Zahidin, Nurul Athirah Mahadi, Adibah Humairah Mahadi Norshamsul, Siti Rubaya Mohd Sharif, Mohd Mukhlis Mahdin, Hairulnizam |
| author_facet | Maskat, Ruhaila Azman, Norazmiera Ayunie Nulizairos, Nur Shaheera Shastera Zahidin, Nurul Athirah Mahadi, Adibah Humairah Mahadi Norshamsul, Siti Rubaya Mohd Sharif, Mohd Mukhlis Mahdin, Hairulnizam |
| author_sort | Maskat, Ruhaila |
| building | UTHM Library |
| collection | Institutional Repository |
| content_provider | Universiti Tun Hussein Onn Malaysia |
| content_source | UTHM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Low-resource languages, like Malay, face the threat of extinction when linguistic resources become scarce. This paper addresses the scarcity issue by contributing to the inventory of low-resource languages, specifically focusing on
Malay-English, known as Manglish. Manglish speakers are
primarily located in Malaysia, Indonesia, Brunei, and Singapore. As global adoption of second languages and social media usage increases, language code-switching, such as Spanglish and Chinglish, becomes more prevalent. In the case of Malay-English, this phenomenon is termed Manglish. To enhance the status of the Malay language and its transition out of the low-resource category, this unique text
corpus, with binary annotations for biological gender and
anonymized author identities is presented. This bi-annotated
dataset offers valuable applications for various fields, including the investigation of cyberbullying, combating gender bias,and providing targeted recommendations for gender-specific products. This corpus can be used with either of the annotations or their composite. The dataset comprises of posts from 50 Malaysian public figures, equally split between biological males and females. The dataset contains a total of 709,012 raw X posts (formerly Twitter), with a relatively balanced distribution of 53.72% from biological female authors and 46.28% from biological male authors. Twitter API was used to scrape the posts. After pre-processing, the total posts reduced to 650,409 posts, widening the gap between the genders with the 56.88% for biological female and 43.12% for biological male. This dataset is a valuable resource for researchers in the field of Malay-English code-switching Natural Language Processing (NLP) and can be used to train or enhance existing and future Manglish language transformers. |
| format | Article |
| id | my.uthm.eprints-11791 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | en |
| publishDate | 2024 |
| publisher | Elsevier |
| record_format | eprints |
| spelling | my.uthm.eprints-117912025-04-29T02:56:24Z http://eprints.uthm.edu.my/11791/ A bi-annotated Malay-English code-switching (Manglish) dataset of X posts for biological gender identification and authorship attribution Maskat, Ruhaila Azman, Norazmiera Ayunie Nulizairos, Nur Shaheera Shastera Zahidin, Nurul Athirah Mahadi, Adibah Humairah Mahadi Norshamsul, Siti Rubaya Mohd Sharif, Mohd Mukhlis Mahdin, Hairulnizam QA Mathematics Low-resource languages, like Malay, face the threat of extinction when linguistic resources become scarce. This paper addresses the scarcity issue by contributing to the inventory of low-resource languages, specifically focusing on Malay-English, known as Manglish. Manglish speakers are primarily located in Malaysia, Indonesia, Brunei, and Singapore. As global adoption of second languages and social media usage increases, language code-switching, such as Spanglish and Chinglish, becomes more prevalent. In the case of Malay-English, this phenomenon is termed Manglish. To enhance the status of the Malay language and its transition out of the low-resource category, this unique text corpus, with binary annotations for biological gender and anonymized author identities is presented. This bi-annotated dataset offers valuable applications for various fields, including the investigation of cyberbullying, combating gender bias,and providing targeted recommendations for gender-specific products. This corpus can be used with either of the annotations or their composite. The dataset comprises of posts from 50 Malaysian public figures, equally split between biological males and females. The dataset contains a total of 709,012 raw X posts (formerly Twitter), with a relatively balanced distribution of 53.72% from biological female authors and 46.28% from biological male authors. Twitter API was used to scrape the posts. After pre-processing, the total posts reduced to 650,409 posts, widening the gap between the genders with the 56.88% for biological female and 43.12% for biological male. This dataset is a valuable resource for researchers in the field of Malay-English code-switching Natural Language Processing (NLP) and can be used to train or enhance existing and future Manglish language transformers. Elsevier 2024 Article PeerReviewed text en http://eprints.uthm.edu.my/11791/1/J17377_a3b15f369ba6e61ca5517eaf40899173.pdf Maskat, Ruhaila and Azman, Norazmiera Ayunie and Nulizairos, Nur Shaheera Shastera and Zahidin, Nurul Athirah and Mahadi, Adibah Humairah Mahadi and Norshamsul, Siti Rubaya and Mohd Sharif, Mohd Mukhlis and Mahdin, Hairulnizam (2024) A bi-annotated Malay-English code-switching (Manglish) dataset of X posts for biological gender identification and authorship attribution. Data in Brief, 52. pp. 1-14. https://doi.org/10.1016/j.dib.2024.110034 |
| spellingShingle | QA Mathematics Maskat, Ruhaila Azman, Norazmiera Ayunie Nulizairos, Nur Shaheera Shastera Zahidin, Nurul Athirah Mahadi, Adibah Humairah Mahadi Norshamsul, Siti Rubaya Mohd Sharif, Mohd Mukhlis Mahdin, Hairulnizam A bi-annotated Malay-English code-switching (Manglish) dataset of X posts for biological gender identification and authorship attribution |
| title | A bi-annotated Malay-English code-switching (Manglish) dataset of X posts for biological gender identification and authorship attribution |
| title_full | A bi-annotated Malay-English code-switching (Manglish) dataset of X posts for biological gender identification and authorship attribution |
| title_fullStr | A bi-annotated Malay-English code-switching (Manglish) dataset of X posts for biological gender identification and authorship attribution |
| title_full_unstemmed | A bi-annotated Malay-English code-switching (Manglish) dataset of X posts for biological gender identification and authorship attribution |
| title_short | A bi-annotated Malay-English code-switching (Manglish) dataset of X posts for biological gender identification and authorship attribution |
| title_sort | bi-annotated malay-english code-switching (manglish) dataset of x posts for biological gender identification and authorship attribution |
| topic | QA Mathematics |
| url | http://eprints.uthm.edu.my/11791/1/J17377_a3b15f369ba6e61ca5517eaf40899173.pdf http://eprints.uthm.edu.my/11791/ https://doi.org/10.1016/j.dib.2024.110034 |
| url_provider | http://eprints.uthm.edu.my/ |