Related papers: COVID-19-related Nepali Tweets Classification in a Low Resource Setting

COVID-19-related Nepali Tweets Classification in a Low Resource Setting

URL: http://arxiv.org/abs/2210.05425v1
Date: Tue, 11 Oct 2022 13:08:37 GMT
Title: COVID-19-related Nepali Tweets Classification in a Low Resource Setting
Authors: Rabin Adhikari, Safal Thapaliya, Nirajan Basnet, Samip Poudel, Aman Shakya, Bishesh Khanal
Abstract summary: We identify the eight most common COVID-19 discussion topics among the Twitter community using the Nepali language. We compare the performance of two state-of-the-art multi-lingual language models for Nepali tweet classification.
Score: 0.15658704610960567
License: http://creativecommons.org/licenses/by/4.0/
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 into 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, they still 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.

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