{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T17:33:39Z","timestamp":1791567219750,"version":"4.3.4"},"reference-count":71,"publisher":"Public Library of Science (PLoS)","issue":"12","license":[{"start":{"date-parts":[[2024,12,19]],"date-time":"2024-12-19T00:00:00Z","timestamp":1734566400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Writing Lab, Institute for the Future of Education, Tecnologico de Monterrey, Mexico"},{"name":"Challenge-Based Research Funding Program 2023","award":["Project ID #IJXT070-23EG99001"],"award-info":[{"award-number":["Project ID #IJXT070-23EG99001"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>\n                    Informal education via social media plays a crucial role in modern learning, offering self-directed and community-driven opportunities to gain knowledge, skills, and attitudes beyond traditional educational settings. These platforms provide access to a broad range of learning materials, such as tutorials, blogs, forums, and interactive content, making education more accessible and tailored to individual interests and needs. However, challenges like information overload and the spread of misinformation highlight the importance of digital literacy in ensuring users can critically evaluate the credibility of information. Consequently, the significance of sentiment analysis has grown in contemporary times due to the widespread utilization of social media platforms as a means for individuals to articulate their viewpoints. Twitter (now X) is well recognized as a prominent social media platform that is predominantly utilized for microblogging. Individuals commonly engage in expressing their viewpoints regarding contemporary events, hence presenting a significant difficulty for scholars to categorize the sentiment associated with such expressions effectively. This research study introduces a highly effective technique for detecting misinformation related to the COVID-19 pandemic. The spread of fake news during the COVID-19 pandemic has created significant challenges for public health and safety because misinformation about the virus, its transmission, and treatments has led to confusion and distrust among the public. This research study introduce highly effective techniques for detecting misinformation related to the COVID-19 pandemic. The\n                    <jats:bold>methodology<\/jats:bold>\n                    of this work includes gathering a dataset comprising fabricated news articles sourced from a corpus and subjected to the natural language processing (NLP) cycle. After applying some filters, a total of five machine learning classifiers and three deep learning classifiers were employed to forecast the sentiment of news articles, distinguishing between those that are authentic and those that are fabricated. This research employs machine learning classifiers, namely Support Vector Machine, Logistic Regression, K-Nearest Neighbors, Decision Trees, and Random Forest, to analyze and compare the obtained results. This research employs Convolutional Neural Networks, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) as deep learning classifiers, and afterwards compares the obtained results. The\n                    <jats:bold>results<\/jats:bold>\n                    indicate that the BiGRU deep learning classifier demonstrates high accuracy and efficiency, with the following indicators: accuracy of 0.91, precision of 0.90, recall of 0.93, and F1-score of 0.92. For the same algorithm, the true negatives, and true positives came out to be 555 and 580, respectively, whereas, the false negatives and false positives came out to be 81, and 68, respectively. In\n                    <jats:bold>conclusion<\/jats:bold>\n                    , this research highlights the effectiveness of the BiGRU deep learning classifier in detecting misinformation related to COVID-19, emphasizing its significance for fostering media literacy and resilience against fake news in contemporary society. The implications of this research are significant for higher education and lifelong learners as it highlights the potential for using advanced machine learning to help educators and institutions in the process of combating the spread of misinformation and promoting critical thinking skills among students. By applying these methods to analyze and classify news articles, educators can develop more effective tools and curricula for teaching media literacy and information validation, equipping students with the skills needed to discern between authentic and fabricated information in the context of the COVID-19 pandemic and beyond. The implications of this research extrapolate to the creation of a society that is resistant to the spread of fake news through social media platforms.\n                  <\/jats:p>","DOI":"10.1371\/journal.pone.0315407","type":"journal-article","created":{"date-parts":[[2024,12,19]],"date-time":"2024-12-19T13:52:26Z","timestamp":1734616346000},"page":"e0315407","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":30,"title":["Machine and deep learning algorithms for sentiment analysis during COVID-19: A vision to create fake news resistant society"],"prefix":"10.1371","volume":"19","author":[{"given":"Muhammad Tayyab","family":"Zamir","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fida","family":"Ullah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3310-432X","authenticated-orcid":true,"given":"Rasikh","family":"Tariq","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Waqas Haider","family":"Bangyal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6141-0204","authenticated-orcid":true,"given":"Muhammad","family":"Arif","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander","family":"Gelbukh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2024,12,19]]},"reference":[{"key":"pone.0315407.ref001","first-page":"101475","article-title":"Informatics","volume":"56","author":"OD Apuke","year":"2021","journal-title":"Fake news and COVID-19: modelling the predictors of fake news sharing among social media users"},{"key":"pone.0315407.ref002","first-page":"1","author":"PD Davidson","year":"2023","journal-title":"Perception of COVID-19 vaccination among Indian Twitter users: computational approach"},{"key":"pone.0315407.ref003","first-page":"32229","volume":"11","author":"RP Ojha","year":"2023","journal-title":"Controlling of Fake Information Dissemination in Online Social Networks: An Epidemiological Approach"},{"key":"pone.0315407.ref004","doi-asserted-by":"crossref","first-page":"6","DOI":"10.4324\/9781315121697-2","article-title":"Social media and education: Reconceptualizing the boundaries of formal and informal learning","author":"C Greenhow","year":"2019","journal-title":"social media and education: Routledge"},{"key":"pone.0315407.ref005","author":"R Jin","year":"2024","journal-title":"Fake News Detection and Manipulation Reasoning via Large Vision-Language Models"},{"issue":"4","key":"pone.0315407.ref006","first-page":"1621","volume":"103","author":"MS Islam","year":"2020","journal-title":"COVID-19\u2013related infodemic and its impact on public health: A global social media analysis"},{"issue":"3","key":"pone.0315407.ref007","first-page":"337","volume":"5","author":"S Loomba","year":"2021","journal-title":"Measuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USA"},{"key":"pone.0315407.ref008","first-page":"914","article-title":"Sentiment Analysis in Online Products Reviews Using Machine Learning","volume":"18","author":"LH Fung","year":"2021","journal-title":"Technology"},{"issue":"6","key":"pone.0315407.ref009","first-page":"434","article-title":"Polarity classification of Twitter data using sentiment analysis","volume":"5","author":"AS Raghuwanshi","year":"2017","journal-title":"International Journal on Recent Innovation Trends in Computing Communication"},{"issue":"7","key":"pone.0315407.ref010","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10462-021-09973-3","article-title":"Systematic reviews in sentiment analysis: a tertiary study","volume":"54","author":"A Ligthart","year":"2021","journal-title":"Artificial Intelligence Review"},{"key":"pone.0315407.ref011","doi-asserted-by":"crossref","unstructured":"Koloski B, Stepi\u0161nik-Perdih T, Pollak S, \u0160krlj B, editors. 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