COVID-19 on YouTube: A Data-Driven Analysis of Sentiment, Toxicity, and Content Recommendations

Citation Author(s):
Vanessa
Su
Emory University
Nirmalya
Thakur
South Dakota School of Mines and Technology
Submitted by:
Nirmalya Thakur
Last updated:
Fri, 11/15/2024 - 21:17
DOI:
10.21227/sbj6-pt91
Data Format:
*.csv
License:
Creative Commons Attribution
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Categories:
Other
Keywords:
YouTube
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Abstract 

Please cite the following paper when using this dataset:

Vanessa Su and Nirmalya Thakur, “COVID-19 on YouTube: A Data-Driven Analysis of Sentiment, Toxicity, and Content Recommendations”, Paper submitted to the IEEE 15th Annual Computing and Communication Workshop and Conference 2025, Las Vegas, USA, Jan 06-08, 2025.

This dataset contains data of YouTube videos about COVID-19 from January 2023 to October 2024. For each video, the video URL, video ID, video title, video description, date of publication, number of likes, number of views, the duration (in seconds), language, tags, categories, and availability of captions, are presented as separate attributes in the dataset.

Instructions: 

Please refer to the above-mentioned paper for details about this dataset

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