StEduCov: A Dataset on Stance Detection in Tweets Towards Online Education During COVID-19 Pandemic

Citation Author(s):
Omama
Hamad
Qatar University
Khaled
Shaban
Qatar University
Ali
Hamdi
University of Adelaide
Submitted by:
Omama Hamad
Last updated:
Fri, 09/16/2022 - 06:52
DOI:
10.21227/99mt-tz89
License:
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Abstract 

StEduCov, a dataset annotated for stances toward online education during the COVID-19 pandemic. StEduCov has 17,097 tweets gathered over 15 months, from March 2020 to May 2021, using Twitter API. The tweets are manually annotated into agree, disagree or neutral classes. We used a set of relevant hashtags and keywords. Specifically, we utilised a combination of hashtags, such as '#COVID 19' or '#Coronavirus' with keywords, such as 'education', 'online learning', 'distance learning' and 'remote learning'. To ensure high annotation quality, three different annotators annotated each tweet and at least one of the reviewers from three judges revised it. They were guided by some instructions, such as that in the case of disagree class, there should be a clear negative statement about online education or its impact. Also, if the tweet is negative but refers to other people (e.g. 'my children hate online learning').

 

Instructions: 

Annotations:

A: Agree

D: Disagree

N: Neutral

 

If you used this dataset please cite us as:

Hamad O, Hamdi A, Hamdi S, Shaban K. StEduCov: An Explored and Benchmarked Dataset on Stance Detection in Tweets towards Online Education during COVID-19 Pandemic. Big Data and Cognitive Computing. 2022; 6(3):88. https://doi.org/10.3390/bdcc6030088

Comments

AGREE

Submitted by Nayanajith Dewage on Tue, 06/28/2022 - 22:06