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StEduCov: An Explored and Benchmarked Dataset on Stance Detection in Tweets towards Online Education during COVID-19 Pandemic
| Content Provider | MDPI |
|---|---|
| Author | Hamad, Omama Hamdi, Ali Hamdi, Sayed Shaban, Khaled |
| Copyright Year | 2022 |
| Description | In 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 |
| Starting Page | 88 |
| e-ISSN | 25042289 |
| DOI | 10.3390/bdcc6030088 |
| Journal | Big Data and Cognitive Computing |
| Issue Number | 3 |
| Volume Number | 6 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-08-22 |
| Access Restriction | Open |
| Subject Keyword | Big Data and Cognitive Computing Cybernetical Science Information and Library Science Text Classification Stance Detection Deep Learning Transfer Learning Covid-19 Pandemic |
| Content Type | Text |
| Resource Type | Article |
