[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127250-en":3,"doc-seo-127250-105":31,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127250,2336475104362,"Eden","https://ap-avatar.wpscdn.com/avatar/22000c4c46a41b752dd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786595829695023868",8,"Research & Report","Comparative analysis of machine learning models for fake news detection in social media - Article","The rapid rise of information sharing on social media has amplified the spread of fake news, making detection increasingly critical for protecting users from misinformation and disinformation. This paper provides a comparative analysis of machine learning models for text-based fake news detection using the ISOT fake news dataset. Gradient boosting reaches the highest accuracy (99.61%), while XGBoost achieves 99.59% with substantially lower execution time, supporting practical real-time deployment. Results highlight trade-offs between accuracy and computational efficiency for scalable systems.","IAES International Journal of Artificial Intelligence (IJ-AI)  \nVol. 14, No. 3, June 2025, pp. 1951∼ 1959  \nISSN: 2252-8938, DOI: 10.11591/ijai.v14.i3.pp1951-1959 ❒ 1951  \n\n| Comparative analysis of machine learning models for fake news detection in social media\u003Cbr>Bahaa Eddine Elbaghazaoui1,2 , Mohamed Amnai2 , Youssef Fakhri2 , Ali Choukri2 , Noreddine Gherabi3\u003Cbr>1National School of Applied Sciences of Sultan Moulay Slimane University, Beni Mellal, Morocco\u003Cbr>2Laboratory of Computer Sciences Research, Faculty of Sciences, Ibn Tofail University Kenitra, Kenitra, Morocco\u003Cbr>3National School of Applied Sciences of Sultan Moulay Slimane University, Khouribga, Morocco |  |  |\n| --- | --- | --- |\n| Article Info\u003Cbr>Article history:\u003Cbr>Received Sep 2, 2024 Revised Nov 22, 2024 Accepted Jan 27, 2025\u003Cbr>Keywords:\u003Cbr>Data profiling\u003Cbr>Fake news Information diffusion Machine learning Social media |  | ABSTRACT\u003Cbr>The rapid rise of information sharing on social media has amplified the spread of fake news, making its detection increasingly critical. As fake news continues to proliferate, the need for efficient detection mechanisms has become more urgent to protect users from misinformation and disinformation. This paper presents a comparative analysis of multiple machine learning models for detecting textbased fake news on social media platforms. Using models such as gradient boosting, XGBoost, and linear support vector classifier (SVC) on the Information Security and Object Technology (ISOT) fake news dataset, the study demonstrates that gradient boosting achieves the highest accuracy of 99.61%, while XGBoost provides a strong balance with 99.59% accuracy and a significantly lower execution time, making it more suitable for real-time applications. These results offer valuable insights into the trade-offs between accuracy and computational efficiency, contributing to the development of more practical detection systems and future research in the field.\u003Cbr>This is an open access article under the CC BY-SA license. |\n| Corresponding Author: |  |  |\n| Bahaa Eddine Elbaghazaoui\u003Cbr>National School of Applied Sciences in Beni Mellal, Sultan Moulay Slimane University Beni Mellal, Morocco\u003Cbr>Email: [b.elbaghazaoui@usms.ac.ma](b.elbaghazaoui@usms.ac.ma) |  |  |\n\n1. INTRODUCTION  \nSocial media users frequently share, like, or repost content, which leads to the widespread dissemination of information, including news from independent authors [1] . This uncontrolled flow of information has contributed to the rise of fake news, a growing global issue [2] . While the concept of fake news is not new, the pervasive use of social media has significantly amplified its impact, facilitating the rapid spread of misinformation [3] .  \nThe urgency of addressing fake news lies in its potential to harm both individuals and society [4] . Fake news, defined as intentionally misleading information disseminated through news outlets or social media, can influence elections, deepen political divides, and shape public perception in dangerous ways [5] . The pervasive spread of fake news undermines the truth within the information ecosystem and misleads readers into accepting biased or inaccurate information, thereby affecting how people interpret real news [6] . In response to this growing problem, this paper addresses the challenge of detecting fake news by applying advanced machine learning algorithms to analyze and classify false information shared on social media platforms. Identifying fake news accurately is essential for preserving the integrity of online discourse and protecting users from misinformation.  \nTraditional computational methods, such as relying on satirical news sources or fact-checking websites, have limitations, including a narrow focus on specific topics like politics and the requirement for human expertise [7] . These constraints hinder the development of models that can generalize across multiple domains, which is necessary for an effective and sc","cbCaiaAHbOLHHKzG","https://ap.wps.com/l/cbCaiaAHbOLHHKzG","pdf",878596,2,1,9,"English","en",105,"# Introduction\n## Motivation and impact of fake news\n## Limitations of traditional approaches\n## Study objective and contributions\n# Methodology and dataset profiling\n## Data collection and cleaning\n## Preprocessing and analysis\n# Model comparison\n## Gradient boosting and XGBoost\n## Linear SVC\n## Accuracy and execution-time evaluation\n# Results and discussion\n## Interpretation vs. prior work\n## Limitations and future directions","[{\"question\":\"What problem does the paper address in social media?\",\"answer\":\"The paper addresses the detection of text-based fake news as misinformation spreads rapidly through social media platforms.\"},{\"question\":\"Which models are compared for fake news detection?\",\"answer\":\"The study compares gradient boosting, XGBoost, and a linear support vector classifier (SVC) using the ISOT fake news dataset.\"},{\"question\":\"What performance trade-off does the paper find between gradient boosting and XGBoost?\",\"answer\":\"Gradient boosting achieves the highest accuracy (99.61%), while XGBoost offers nearly comparable accuracy (99.59%) with much lower execution time, making it more suitable for real-time applications.\"}]","Comparative analysis of machine learning models for fake news detection in social media - Article | PDF",1785937746,23,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"comparative-analysis-of-machine-learning-models-for-fake-news-detection-in-social-media-article","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/comparative-analysis-of-machine-learning-models-for-fake-news-detection-in-social-media-article/127250/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does the paper address in social media?","Question",{"text":76,"@type":77},"The paper addresses the detection of text-based fake news as misinformation spreads rapidly through social media platforms.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which models are compared for fake news detection?",{"text":81,"@type":77},"The study compares gradient boosting, XGBoost, and a linear support vector classifier (SVC) using the ISOT fake news dataset.",{"name":83,"@type":74,"acceptedAnswer":84},"What performance trade-off does the paper find between gradient boosting and XGBoost?",{"text":85,"@type":77},"Gradient boosting achieves the highest accuracy (99.61%), while XGBoost offers nearly comparable accuracy (99.59%) with much lower execution time, making it more suitable for real-time applications.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,128,131,135],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":22,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":126,"slug":127},"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]