{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T16:49:46Z","timestamp":1772556586963,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,11,3]],"date-time":"2023-11-03T00:00:00Z","timestamp":1698969600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University","award":["GRANT4,967"],"award-info":[{"award-number":["GRANT4,967"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>The online spread of fake news on various platforms has emerged as a significant concern, posing threats to public opinion, political stability, and the dissemination of reliable information. Researchers have turned to advanced technologies, including machine learning (ML) and deep learning (DL) techniques, to detect and classify fake news to address this issue. This research study explores fake news classification using diverse ML and DL approaches. We utilized a well-known \u201cFake News\u201d dataset sourced from Kaggle, encompassing a labelled news collection. We implemented diverse ML models, including multinomial na\u00efve bayes (MNB), gaussian na\u00efve bayes (GNB), Bernoulli na\u00efve Bayes (BNB), logistic regression (LR), and passive aggressive classifier (PAC). Additionally, we explored DL models, such as long short-term memory (LSTM), convolutional neural networks (CNN), and CNN-LSTM. We compared the performance of these models based on key evaluation metrics, such as accuracy, precision, recall, and the F1 score. Additionally, we conducted cross-validation and hyperparameter tuning to ensure optimal performance. The results provide valuable insights into the strengths and weaknesses of each model in classifying fake news. We observed that DL models, particularly LSTM and CNN-LSTM, showed better performance compared to traditional ML models. These models achieved higher accuracy and demonstrated robustness in classification tasks. These findings emphasize the potential of DL models to tackle the spread of fake news effectively and highlight the importance of utilizing advanced techniques to address this challenging problem.<\/jats:p>","DOI":"10.3390\/a16110507","type":"journal-article","created":{"date-parts":[[2023,11,3]],"date-time":"2023-11-03T10:59:54Z","timestamp":1699009194000},"page":"507","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Deep Dive into Fake News Detection: Feature-Centric Classification with Ensemble and Deep Learning Methods"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6598-6240","authenticated-orcid":false,"given":"Fawaz Khaled","family":"Alarfaj","sequence":"first","affiliation":[{"name":"Department of Management Information Systems, School of Business King Faisal University (KFU), Al-Ahsa 31982, Saudi Arabia"}]},{"given":"Jawad Abbas","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Computer Science, COMSATS University, Wah Campus, Rawalpindi 47040, Pakistan"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"15311","DOI":"10.1007\/s10489-022-04266-w","article-title":"A mutual attention based multimodal fusion for fake news detection on social network","volume":"53","author":"Guo","year":"2023","journal-title":"Appl. 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