{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T18:47:11Z","timestamp":1771613231179,"version":"3.50.1"},"reference-count":48,"publisher":"Wiley","license":[{"start":{"date-parts":[[2022,7,7]],"date-time":"2022-07-07T00:00:00Z","timestamp":1657152000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Computational Intelligence and Soft Computing"],"published-print":{"date-parts":[[2022,7,7]]},"abstract":"<jats:p>In recent years, COVID-19 has been regarded as the most dangerous pandemic for several countries. On various social media platforms, such as Twitter, Facebook, and Instagram, a variety of rumours, hypes, and news are published. This might have a detrimental impact on people\u2019s life. As a result, social media platforms have always had a difficult time authenticating this fake information. Different machine learning (ML) and deep learning (DL) classifiers were used in this work to categorize the continuing impacts of tweets and forecast their after-effects. Support vector machine (SVM), random forest (RF), decision tree (DT), and k-nearest neighbor (KNN) were used for classification, while AdaBoost and convolutional neural network (CNN) were utilized for future effects. The tweets dataset from Kaggle was used to train the SVM, RF, KNN, and DT models, which were then assessed on multiple evaluation criteria such as accuracy, precision, recall, and F1-score, using a 70\u2009:\u200930 ratio. The CNN and AdaBoost, on the other hand, have been taught to detect the mean square error, root mean square error, and mean absolute error. With 0.74 and 0.73 percent score out of 1, respectively, RF and SVM exhibit the best accuracy in impact when classifying the outcomes on the obtained dataset. In terms of a regression problem, CNN beat the ADA Regressor across the board.<\/jats:p>","DOI":"10.1155\/2022\/1209172","type":"journal-article","created":{"date-parts":[[2022,7,7]],"date-time":"2022-07-07T23:20:24Z","timestamp":1657236024000},"page":"1-8","source":"Crossref","is-referenced-by-count":7,"title":["COVID-19 Tweets Classification during Lockdown Period Using Machine Learning Classifiers"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0470-0576","authenticated-orcid":true,"given":"Syed Ali","family":"Jafar Zaidi","sequence":"first","affiliation":[{"name":"Institute of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9242-8888","authenticated-orcid":true,"given":"Indranath","family":"Chatterjee","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Tongmyong University, Busan 48520, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2336-0490","authenticated-orcid":true,"given":"Samir","family":"Brahim Belhaouari","sequence":"additional","affiliation":[{"name":"Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"crossref","DOI":"10.1016\/j.jvb.2020.103436","article-title":"Unemployment in the time of COVID-19: a research agenda, journal of vocational behaviour","volume":"119","author":"D. 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