Preprint Data Descriptor Version 2 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 9 June 2022 / Approved: 10 June 2022 / Online: 10 June 2022 (03:42:25 CEST)
Version 2 : Received: 20 July 2022 / Approved: 21 July 2022 / Online: 21 July 2022 (08:05:19 CEST)

A peer-reviewed article of this Preprint also exists.

Thakur, N. A Large-Scale Dataset of Twitter Chatter about Online Learning during the Current COVID-19 Omicron Wave. Data 2022, 7, 109. Thakur, N. A Large-Scale Dataset of Twitter Chatter about Online Learning during the Current COVID-19 Omicron Wave. Data 2022, 7, 109.

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. Mining such conversations, such as Tweets, to develop a dataset can serve as a data resource for interdisciplinary research 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 public Twitter dataset of conversations about online learning 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.

Keywords

COVID-19; COVID; Omicron; online learning; remote learning; online education; Twitter; dataset; Tweets; social media; Big Data

Subject

Computer Science and Mathematics, Information Systems

Comments (1)

Comment 1
Received: 21 July 2022
Commenter: Nirmalya Thakur
Commenter's Conflict of Interests: Author
Comment: The following are the changes that have been made in this version of the preprint:
1. The dataset proposed in this paper has been updated to include TweetIDs of the relevant Tweets posted until July 13, 2022. The DOI of the new version of the dataset has been included in the paper.
2. Section 3.1 has been revised to include additional details about the methodology that was followed for the development of this dataset.
3. Table 2 has been updated to present the names of the revised dataset files.
4. A new Table – Table 3 has been added that presents different characteristic features of this dataset.
+ Respond to this comment

We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.

Leave a public comment
Send a private comment to the author(s)
* All users must log in before leaving a comment
Views 0
Downloads 0
Comments 1
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.