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AuthorHamad, Omama
AuthorHamdi, Ali
AuthorHamdi, Sayed
AuthorShaban, Khaled
Available date2022-12-21T10:01:48Z
Publication Date2022
Publication NameBig Data and Cognitive Computing
ResourceScopus
URIhttp://dx.doi.org/10.3390/bdcc6030088
URIhttp://hdl.handle.net/10576/37527
AbstractIn this paper, we present StEduCov, an annotated dataset for the analysis of stances toward online education during the COVID-19 pandemic. StEduCov consists of 16,572 tweets gathered over 15 months, from March 2020 to May 2021, using the Twitter API. The tweets were manually annotated into the classes agree, disagreeor neutral. We performed benchmarking on the dataset using state-of-the-art and traditional machine learning models. Specifically, we trained deep learning models-bidirectional encoder representations from transformers, long short-term memory, convolutional neural networks, attention-based biLSTM and Naive Bayes SVM-in addition to naive Bayes, logistic regression, support vector machines, decision trees, K-nearest neighbor and random forest. The average accuracy in the 10-fold cross-validation of these models ranged from 75% to (Formula presented.) % and from (Formula presented.) % to 68% for binary and multi-class stance classifications, respectively. Performances were affected by high vocabulary overlaps between classes and unreliable transfer learning using deep models pre-trained on general texts in relation to specific domains such as COVID-19 and distance education. 2022 by the authors.
Languageen
PublisherMDPI
SubjectCOVID-19 pandemic
deep learning
stance detection
text classification
transfer learning
TitleStEduCov: An Explored and Benchmarked Dataset on Stance Detection in Tweets towards Online Education during COVID-19 Pandemic
TypeArticle
Issue Number3
Volume Number6
dc.accessType Open Access


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