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

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  • Nirmalya Thakur

    (Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, OH 45221-0030, USA)

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.

Suggested Citation

  • Nirmalya Thakur, 2022. "A Large-Scale Dataset of Twitter Chatter about Online Learning during the Current COVID-19 Omicron Wave," Data, MDPI, vol. 7(8), pages 1-16, August.
    • Handle: RePEc:gam:jdataj:v:7:y:2022:i:8:p:109-:d:880401
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    References listed on IDEAS

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    1. Fahd A. Ghanem & M. C. Padma & Ramez Alkhatib, 2023. "Automatic Short Text Summarization Techniques in Social Media Platforms," Future Internet, MDPI, vol. 15(9), pages 1-27, September.

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