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

arXiv: https://arxiv.org/abs/2208.07810 · HTML: https://mlsanity.com/paper/2208.07810/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 140 references that are in the map.
- First posted on arXiv 20 Jul 2022.

## Builds on (most cited first, 7 of 7)

- [CoAID: COVID-19 Healthcare Misinformation Dataset](https://mlsanity.com/paper/2006.00885/) (2020)
- [#Election2020: The First Public Twitter Dataset on the 2020 US Presidential Election](https://mlsanity.com/paper/2010.00600/) (2020)
- [ALONE: A Dataset for Toxic Behavior among Adolescents on Twitter](https://mlsanity.com/paper/2008.06465/) (2020)
- [Twitter Dataset for 2022 Russo-Ukrainian Crisis](https://mlsanity.com/paper/2203.02955/) (2022)
- [Social Media Mining Toolkit (SMMT)](https://mlsanity.com/paper/2003.13894/) (2020)
- [Natural Hazards Twitter Dataset](https://mlsanity.com/paper/2004.14456/) (2020)
- [A large-scale Twitter dataset for drug safety applications mined from publicly existing resources](https://mlsanity.com/paper/2003.13900/) (2020)

## Built on by (most cited first, 8 of 8)

- [Sentiment Analysis and Text Analysis of the Public Discourse on Twitter about COVID-19 and MPox](https://mlsanity.com/paper/2312.10580/) (2023)
- [A Domain-Agnostic Neurosymbolic Approach for Big Social Data Analysis: Evaluating Mental Health Sentiment on Social Media during COVID-19](https://mlsanity.com/paper/2411.07163/) (2024)
- [Analyzing Public Reactions, Perceptions, and Attitudes during the MPox Outbreak: Findings from Topic Modeling of Tweets](https://mlsanity.com/paper/2312.11895/) (2023)
- [COVID-19 on YouTube: A Data-Driven Analysis of Sentiment, Toxicity, and Content Recommendations](https://mlsanity.com/paper/2412.17180/) (2024)
- [Five Years of COVID-19 Discourse on Instagram: A Labeled Instagram Dataset of Over Half a Million Posts for Multilingual Sentiment Analysis](https://mlsanity.com/paper/2410.03293/) (2024)
- [Quantifying Public Response to COVID-19 Events: Introducing the Community Sentiment and Engagement Index](https://mlsanity.com/paper/2412.16925/) (2024)
- [Mpox Narrative on Instagram: A Labeled Multilingual Dataset of Instagram Posts on Mpox for Sentiment, Hate Speech, and Anxiety Analysis](https://mlsanity.com/paper/2409.05292/) (2024)
- [Systematic Classification of Studies Investigating Social Media Conversations about Long COVID Using a Novel Zero-Shot Transformer Framework](https://mlsanity.com/paper/2503.11845/) (2025)

## Abstract (arXiv)

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.