[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290587-105":59,"doc-detail-290587-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","a-machine-learning-technique-for-detection-of-social-media-fake-news-volume-19-issue-1","A Machine Learning Technique for Detection of Social Media Fake News - Volume 19 - Issue 1","","The rise of the Internet and social media platforms has enabled rapid dissemination of deceptive information, producing large volumes of potentially misleading data. Automated classification of news articles as misinformation is critical because fake news can create confusion and contribute to psychological distress. This study proposes a machine learning workflow that filters social media data, preprocesses it, trains a classification model, and evaluates system performance to determine whether news is true or false. 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Consumers generate and share more information and a massive amount of data than ever with the growing utilization of social media sites, many of which are deceptive with little relevance to reality. A daunting task is the automated classification of a text article as misleading or misinformation. To see the latest news alerts, individuals often utilize e-newspapers, Twitter, Instagram, Youtube, and many more. Fake news created on social media can lead to uncertainty amongst individuals and psychiatric illness. We may detect that news obtained based on machine learning techniques is either true or false. This study proposes a machine learning technique to detect fake news by carrying out filtration on social media data, classifying the preprocessed data using a machine learning algorithm, evaluating the developed system, and evaluating the results.  \nKeyWoRDS  \nDetection Techniques, Fake News, Machine Learning, Social Media, Social Network  \n1. INTRoDUCTIoN  \nSocial media platforms have recently become a primary source of news and information (Wang et al., 2022; Li, Zhou & Huang, 2021) . However, the ease of sharing and spreading information on social media has led to an increase in the spread of fake news, which can significantly impact public opinion, politics, and society (Apuke & Omar, 2021; Wang et al., 2023; Naeem, Bhatti & Khan, 2021; Meng, Xiao & Wang, 2022) . Detecting fake news in email is challenging, as the content can be diverse, misleading, and constantly evolving (Zhang & Ghorbani, 2020; Huang, 2020; Li et al., 2022). Machine learning algorithms have shown promising results in detecting fake news on email platforms (Najadat, Tawalbeh & Awawdeh, 2022) and other domains (Zhou et al., 2022; Zheng et  \nDOI: 10.4018/IJSWIS.326120 *Corresponding Author  \nThis article published as an Open Access article distributed under the terms of the Creative Commons Attribution License ([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)) which permits unrestricted use, distribution, and production in any medium, provided the author of the original work and original publication source are properly credited.  \nInternational Journal on Semantic Web and Information Systems  \nVolume 19 • Issue 1  \nal., 2021; Zheng & Yin, 2022; Chen, Chen & Lu, 2023) . This paper presents a machine-learning technique for detecting fake email news using the Support Vector Machine with Radial Basis Function (SVM-RBF) and K-Nearest Neighbor (KNN) algorithms.  \nFake news has become a significant problem on email and other platforms, with the potential to cause harm to individuals and society (Zhang & Ghorbani, 2020) . The spread of fake news can lead to confusion, misinterpretation, and damage to reputation. Fake news can also be used to manipulate public opinion and affect the outcome of political processes (Gradon, 2020; Guo & Zhong, 2022) . Hence, detecting and preventing phony news spread is crucial to maintaining a healthy and informed society. Several techniques have been proposed to detect fake news in spam emails, including manual fact-checking, crowdsourcing, and machine learning (Chen, Lai & Lian, 2022). Manual fact-checking and crowdsourcing techniques can be time-consuming, costly, ","cbCainruXrRKa8bc","https://ap.wps.com/l/cbCainruXrRKa8bc","pdf",763123,25,"English","# Abstract\n## Introduction","[{\"question\":\"Why is detecting fake news on social media important?\",\"answer\":\"Social media makes sharing and spreading information easy, which increases the spread of fake news. It can negatively affect public opinion, politics, society, and individual wellbeing.\"},{\"question\":\"What detection approach does the study propose?\",\"answer\":\"The study proposes a machine learning technique that filters social media data, preprocesses the data, classifies it using a machine learning algorithm, and evaluates the developed system and results.\"},{\"question\":\"What algorithms are mentioned for detecting fake email news?\",\"answer\":\"The paper presents a machine-learning technique using Support Vector Machine with Radial Basis Function (SVM-RBF) and K-Nearest Neighbor (KNN) algorithms.\"}]","A Machine Learning Technique for Detection of Social Media Fake News - Volume 19 - Issue 1 | PDF",63]