[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124219-en":3,"doc-seo-124219-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124219,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","A Comprehensive Review of Fake News Detection Approaches Using Machine Learning","The rapid spread of fake news across digital platforms has created pressing demands for protecting information integrity in public discourse. As the influence of fabricated stories escalates, dependable detection strategies become increasingly critical. This paper provides a comprehensive review of machine learning–based fake news detection processes, tracing the shift from traditional algorithms to NLP methods and advanced deep learning models. It evaluates key stages such as data preprocessing, feature extraction, and contextual or network-driven integration to improve accuracy and robustness, while discussing hybrid and ensemble approaches and challenges related to dataset quality, interpretability, and scalability, and outlines future research directions toward more powerful, transparent, and scalable solutions.","A Comprehensive Review of Fake News Detection Approaches Using  \nMachine Learning  \nDr. Kingsley M. Okorie  \nDepartment of Computer Science, Godfrey Okoye University, Nigeria  \nDr. Uchenna Franklin Okebanama  \nDepartment of Computer Science, Godfrey Okoye University, Nigeria Ikenna Victor Ellam 􀀍  \nDepartment of Computer Science, Godfrey Okoye University, Nigeria  \nArticle History:  \nReceived: 24.02.2025 Revised: 01.04.2025 Accepted: 04.04.2025 Published: 09.04.2025  \nAbstract  \nThe speedy dissemination of fake news throughout virtual systems has created huge demanding situations in retaining the integrity of records within the public sphere. Because the impact of fabricated information tales grows, the need for dependable detection strategies has turn out to be increasingly important. This paper presents a comprehensive review of numerous processes to fakenews detection with the use of machine learning. It explores the evolution of detection strategies, together with conventional machine learning algorithms, natural language processing (NLP) techniques, and superior deep learning models. The review examines key components consisting of data preprocessing, feature extraction, and the integration of contextual and network-primarily based information to enhance the accuracy and robustness of detection systems. Moreover, the paper discusses the position of hybrid models and ensemble techniques in enhancing overall performance and addresses the demanding situations associated with dataset high-quality, model interpretability, and scalability. through synthesizing modernday research, this review identifies existing gaps and indicates potential guidelines for future work, emphasizing the significance of developing more powerful, obvious, and scalable solutions to counter the unfold of fake information.  \nKeywords: machine learning (ML), support vector machines (SVM), natural language processing (NLP), Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM).  \nSuggested citation: Okorie, K.M., Okebanama, U.F., & Ellam, I.V. (2025) . A Comprehensive Review of Fake News Detection Approaches Using Machine Learning. European Journal ofTheoretical and Applied Sciences, 3(2), 497-510. [https://doi.org/10.59324/ejtas.2025.3](https://doi.org/10.59324/ejtas.2025.3) (2).41  \nIntroduction  \nPrevious research have shown that machine learning strategies, which includes support vector machines (SVM), decision trees, and  \nneural networks, can successfully parent among actual and fake news based totally on numerous capabilities like text content, source credibility, and user engagement metrics. (Pacheco, et al.,  \n2018; Li, et al., 2020) . In addition, new models that improve the accuracy and robustness of fake news detection systems have been introduced through recent advances in natural language processing (NLP) and deep learning (Devlin, 2019; Liu, 2021) .  \nThis review aims to deliver an intensive examination of various automated and ML processes applied in fake news detection, exploring their methodologies, overall performance metrics, and applicability across different contexts. Via synthesizing findings from latest studies, we aim to provide a complete review that informs future studies and practical packages in this crucial area.  \nLiterature Review  \nEarly work in fake information detection mostly utilized conventional machine learning algorithms. These methods focus on extracting functions from information articles and then classifying them into \"fake\" or \"real” and,“verified” or “unverified” categories. One of the earliest and only techniques used for textual content type is the Naive Bayes classifier (Zhang & Zhao, 2015) carried out this approach to news articles, achieving reasonable overall performance but with boundaries in dealing with complicated linguistic patterns. Support Vector Machines (SVM) has been used notably in text classification due to their effectiveness in excessive-dimensional areas. (Gupta et al., 2018) ","cbCaieCraHvi0MG0","https://ap.wps.com/l/cbCaieCraHvi0MG0","pdf",466800,1,14,"English","en",105,"# Abstract\n# Introduction\n# Literature Review\n## Evolution of Detection Technique\n## Hybrid and Ensemble Strategies","[{\"question\":\"What does the review cover about fake news detection approaches?\",\"answer\":\"It reviews multiple machine learning methods for fake news detection, including conventional algorithms, NLP-based techniques, and deep learning models. It also discusses how different information sources and context are incorporated to improve performance.\"},{\"question\":\"Which main components are highlighted as important for detection systems?\",\"answer\":\"The review emphasizes data preprocessing, feature extraction, and the integration of contextual and network-based information. These components are presented as key to improving accuracy and robustness.\"},{\"question\":\"What challenges and future directions does the paper discuss?\",\"answer\":\"It discusses challenges involving dataset quality, model interpretability, and scalability. By synthesizing recent research, it identifies gaps and suggests directions for developing more powerful, transparent, and scalable solutions.\"}]","A Comprehensive Review of Fake News Detection Approaches Using Machine Learning | PDF",1785821072,35,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"a-comprehensive-review-of-fake-news-detection-approaches-using-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-comprehensive-review-of-fake-news-detection-approaches-using-machine-learning/124219/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What does the review cover about fake news detection approaches?","Question",{"text":75,"@type":76},"It reviews multiple machine learning methods for fake news detection, including conventional algorithms, NLP-based techniques, and deep learning models. It also discusses how different information sources and context are incorporated to improve performance.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which main components are highlighted as important for detection systems?",{"text":80,"@type":76},"The review emphasizes data preprocessing, feature extraction, and the integration of contextual and network-based information. These components are presented as key to improving accuracy and robustness.",{"name":82,"@type":73,"acceptedAnswer":83},"What challenges and future directions does the paper discuss?",{"text":84,"@type":76},"It discusses challenges involving dataset quality, model interpretability, and scalability. 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