A Large-Scale Dataset of Twitter Chatter about Online Learning during
the Current COVID-19 Omicron Wave
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 Omicron variant
in November 2021. The dataset is compliant with the privacy policy,
developer agreement, and guidelines for content redistribution of
Twitter, as well as with the FAIR principles (Findability,
Accessibility, Interoperability, and Reusability) principles for
scientific data management. The paper also briefly outlines some
potential applications in the fields of Big Data, Data Mining, Natural
Language Processing, and their related disciplines, with a specific
focus on online learning during this Omicron wave that may be studied,
explored, and investigated by using this dataset.