COVID-19-related Nepali Tweets Classification in a Low Resource Setting
Rabin Adhikari, Safal Thapaliya, Nirajan Basnet, Samip Poudel, Aman Shakya, Bishesh Khanal
Abstract
Billions of people across the globe have been using social media platforms in their local languages to voice their opinions about the various topics related to the COVID-19 pandemic. Several organizations, including the World Health Organization, have developed automated social media analysis tools that classify COVID-19-related tweets to various topics. However, these tools that help combat the pandemic are limited to very few languages, making several countries unable to take their benefit. While multi-lingual or low-resource language-specific tools are being developed, there is still a need to expand their coverage, such as for the Nepali language. In this paper, we identify the eight most common COVID-19 discussion topics among the Twitter community using the Nepali language, set up an online platform to automatically gather Nepali tweets containing the COVID-19-related keywords, classify the tweets into the eight topics, and visualize the results across the period in a web-based dashboard. We compare the performance of two state-of-the-art multi-lingual language models for Nepali tweet classification, one generic (mBERT) and the other Nepali language family-specific model (MuRIL). Our results show that the models’ relative performance depends on the data size, with MuRIL doing better for a larger dataset. The annotated data, models, and the web-based dashboard are open-sourced at https://github.com/naamiinepal/covid-tweet-classification.- Anthology ID:
- 2022.smm4h-1.52
- Volume:
- Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Graciela Gonzalez-Hernandez, Davy Weissenbacher
- Venue:
- SMM4H
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 209–215
- Language:
- URL:
- https://aclanthology.org/2022.smm4h-1.52
- DOI:
- Cite (ACL):
- Rabin Adhikari, Safal Thapaliya, Nirajan Basnet, Samip Poudel, Aman Shakya, and Bishesh Khanal. 2022. COVID-19-related Nepali Tweets Classification in a Low Resource Setting. In Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task, pages 209–215, Gyeongju, Republic of Korea. Association for Computational Linguistics.
- Cite (Informal):
- COVID-19-related Nepali Tweets Classification in a Low Resource Setting (Adhikari et al., SMM4H 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2022.smm4h-1.52.pdf
- Code
- naamiinepal/covid-tweet-classification