{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T15:09:25Z","timestamp":1784646565832,"version":"3.55.0"},"reference-count":34,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T00:00:00Z","timestamp":1729036800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>The creation and spreading of fake information can be carried out very easily through the internet community. This pervasive escalation of fake news and rumors has an extremely adverse effect on the nation and society. Detecting fake news on the social web is an emerging topic in research today. In this research, the authors review various characteristics of fake news and identify research gaps. In this research, the fake news dataset is modeled and tokenized by applying term frequency and inverse document frequency (TFIDF). Several machine-learning classification approaches are used to compute evaluation metrics. The authors proposed hybridizing SVMs and RF classification algorithms for improved accuracy, precision, recall, and F1-score. The authors also show the comparative analysis of different types of news categories using various machine-learning models and compare the performance of the hybrid RFSVM. Comparative studies of hybrid RFSVM with different algorithms such as Random Forest (RF), na\u00efve Bayes (NB), SVMs, and XGBoost have shown better results of around 8% to 16% in terms of accuracy, precision, recall, and F1-score.<\/jats:p>","DOI":"10.3390\/a17100459","type":"journal-article","created":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T07:28:35Z","timestamp":1729495715000},"page":"459","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Hybrid RFSVM: Hybridization of SVM and Random Forest Models for Detection of Fake News"],"prefix":"10.3390","volume":"17","author":[{"given":"Deepali Goyal","family":"Dev","sequence":"first","affiliation":[{"name":"GGSIPU, NSUT East Campus (Formerly Ambedkar Institute of Advanced Communication Technologies and Research), New Delhi 110031, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vishal","family":"Bhatnagar","sequence":"additional","affiliation":[{"name":"NSUT East Campus (Formerly Ambedkar Institute of Advanced Communication Technologies and Research), New Delhi 110031, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,16]]},"reference":[{"key":"ref_1","unstructured":"Zhou, X., and Zafarani, R. (2018). Fake news: A survey of research, detection methods, and opportunities. arXiv."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1007\/s11633-019-1216-5","article-title":"Text-mining-based Fake News Detection Using Ensemble Methods","volume":"17","author":"Reddy","year":"2020","journal-title":"Int. J. Autom. Comput."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1145\/3386253","article-title":"FNED: A Deep Network for Fake News Early Detection on Social Media","volume":"38","author":"Liu","year":"2020","journal-title":"ACM TOIS"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"102018","DOI":"10.1016\/j.ipm.2019.02.016","article-title":"Detecting breaking news rumors of emerging topics in social media","volume":"57","author":"Alkhodair","year":"2020","journal-title":"Inf. Process. Manag."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"112986","DOI":"10.1016\/j.eswa.2019.112986","article-title":"Fake News, Rumor, Information Pollution in Social Media and Web: A Contemporary Survey of State-of-the-arts, Challenges and Opportunities","volume":"153","author":"Meel","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_6","first-page":"21","article-title":"Combating fake news: A survey on identification and mitigation techniques","volume":"10","author":"Sharma","year":"2019","journal-title":"ACM TIST"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.ins.2019.05.035","article-title":"A survey on fake news and rumour detection techniques","volume":"497","author":"Bondielli","year":"2019","journal-title":"Inf. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"123174","DOI":"10.1016\/j.physa.2019.123174","article-title":"Fake news detection within online social media using supervised artificial intelligence algorithms","volume":"540","author":"Ozbay","year":"2020","journal-title":"Phys. A Stat. Mech. Appl"},{"key":"ref_9","unstructured":"Monti, F., Frasca, F., Eynard, D., Mannion, D., and Bronstein, M.M. (2019). Fake News Detection on Social Media Using Geometric Deep Learning. arXiv."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.cogsys.2019.07.004","article-title":"Detection and veracity analysis of fake news via scrapping and authenticating the web search","volume":"58","author":"Vishwakarma","year":"2019","journal-title":"Cogn. Syst. Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.procs.2018.10.495","article-title":"Detecting rumors in social media: A survey","volume":"142","author":"Alzanin","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/MIS.2018.2877280","article-title":"AI and fake news","volume":"33","author":"Cybenko","year":"2018","journal-title":"IEEE Intell. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.chb.2018.02.032","article-title":"A computational approach for xamining the roots and spreading patterns of fake news: Evolution tree analysis","volume":"84","author":"Jang","year":"2018","journal-title":"Comput. Hum. Behav."},{"key":"ref_14","unstructured":"P\u00e9rez-Rosas, V., Kleinberg, B., Lefevre, A., and Mihalcea, R. (2017). Automatic Detection of Fake News. arXiv."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wang, W.Y. (2017). \u201cLiar, liar pants on fire\u201d: A new benchmark dataset for fake news detection. arXiv.","DOI":"10.18653\/v1\/P17-2067"},{"key":"ref_16","unstructured":"Ruchansky, N., Seo, S., and Liu, Y. (2017, January 6\u201310). CSI: A hybrid deep model for fake news detection. Proceedings of the 26th ACM International Conference on Information and Knowledge Management, Singapore."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1145\/3137597.3137600","article-title":"Fake news detection on social media: A data mining perspective","volume":"19","author":"Shu","year":"2017","journal-title":"ACM SIGKDD Explor. Newsl."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1016\/j.procs.2018.01.150","article-title":"Random forest and support vector machine based hybrid approach to sentiment analysis","volume":"127","author":"Lazaar","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Garg, N., Gupta, R., Kaur, M., Ahmed, S., and Shankar, H. (2023, January 11\u201312). Efficient Detection and Classification of Orange Diseases using Hybrid CNN-SVM Model. Proceedings of the 2023 International Conference on Disruptive Technologies (ICDT), Greater Noida, India.","DOI":"10.1109\/ICDT57929.2023.10150721"},{"key":"ref_20","first-page":"100007","article-title":"Fake news detection: A hybrid CNN-RNN based deep learning approach","volume":"1","author":"Nasir","year":"2021","journal-title":"Int. J. Inf. Manag. Data Insights"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Dedeepya, P., Yarrarapu, M., Kumar, P.P., Kaushik, S.K., Raghavendra, P.N., and Chandu, P. (2024, January 5\u20137). Fake News Detection on Social Media Through a Hybrid SVM-KNN Approach Leveraging Social Capital Variables. Proceedings of the 2024 3rd International Conference on Applied Artificial Intelligence and Computing (ICAAIC), Salem, India.","DOI":"10.1109\/ICAAIC60222.2024.10575681"},{"key":"ref_22","unstructured":"Ramos, J. (2024, August 11). Using TF-IDF to determine word relevance in document queries. In Proceedings of the 1st Instructional Conference on Machine Learning. Available online: https:\/\/citeseerx.ist.psu.edu\/document?repid=rep1;type=pdf;doi=b3bf6373ff41a115197cb5b30e57830c16130c2c."},{"key":"ref_23","unstructured":"Rish, I. (2001, January 4). An empirical study of the naive Bayes classifier. Proceedings of the IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, Seattle, WA, USA."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1961189.1961199","article-title":"LIBSVM: A library for support vector machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM TIST"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1016\/j.ins.2004.12.006","article-title":"An extension of the naive Bayesian classifier","volume":"176","author":"Yager","year":"2006","journal-title":"Inf. Sci"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_28","first-page":"8885861","article-title":"Fake news detection using machine learning ensemble methods","volume":"1","author":"Ahmad","year":"2020","journal-title":"Complexity"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1016\/j.protcy.2016.08.114","article-title":"Student academic performance prediction model using decision tree and fuzzy genetic algorithm","volume":"25","author":"Hamsa","year":"2016","journal-title":"Proc. Technol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"42037","DOI":"10.1007\/s11042-023-17301-w","article-title":"Fake News Detection Using Ensemble Techniques","volume":"83","author":"Malhotra","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref_31","first-page":"509","article-title":"Fake news detection using machine learning algorithms","volume":"8","author":"Sharma","year":"2020","journal-title":"IJCRT"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"012040","DOI":"10.1088\/1757-899X\/1099\/1\/012040","article-title":"Fake news detection using machine learning approaches","volume":"1099","author":"Khanam","year":"2021","journal-title":"IOP Conf. Ser. Mater. Sci. Eng."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"012027","DOI":"10.1088\/1742-6596\/2161\/1\/012027","article-title":"Fake news detection from online media using machine learning classifiers","volume":"2161","author":"Pandey","year":"2022","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"4451","DOI":"10.1007\/s12652-023-04562-4","article-title":"A cooperative deep learning model for fake news detection in online social networks","volume":"14","author":"Mallick","year":"2023","journal-title":"J. Ambient. Intell. Humaniz. Comput."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/17\/10\/459\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:14:07Z","timestamp":1760112847000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/17\/10\/459"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,16]]},"references-count":34,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["a17100459"],"URL":"https:\/\/doi.org\/10.3390\/a17100459","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,16]]}}}