A Large-Scale Dataset of Twitter Chatter about Online Learning during the Current COVID-19 Omicron Wave
Nirmalya Thakur

TL;DR
This paper introduces a large, open-access Twitter dataset capturing online learning discussions during the COVID-19 Omicron wave, enabling diverse research in social media analysis and online education.
Contribution
It provides a comprehensive, privacy-compliant dataset of tweets about online learning during the Omicron surge, facilitating research in NLP, data mining, and educational studies.
Findings
Dataset covers global online learning conversations since November 2021
Ensures compliance with Twitter policies and FAIR data principles
Supports research in Big Data, NLP, and educational analysis
Abstract
The COVID-19 Omicron variant, reported to be the most immune evasive variant of COVID-19, is resulting in a surge of COVID-19 cases globally. This has caused schools, colleges, and universities in different parts of the world to transition to online learning. As a result, social media platforms such as Twitter are seeing an increase in conversations related to online learning in the form of tweets. Mining such tweets to develop a dataset can serve as a data resource for different applications and use-cases related to the analysis of interest, views, opinions, perspectives, attitudes, and feedback towards online learning during the current surge of COVID-19 cases caused by the Omicron variant. Therefore, this work presents a large-scale open-access Twitter dataset of conversations about online learning from different parts of the world since the first detected case of the COVID-19…
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Taxonomy
TopicsMisinformation and Its Impacts · COVID-19 diagnosis using AI · COVID-19 Digital Contact Tracing
