[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122883-en":3,"doc-seo-122883-105":29,"detail-sidebar-cat-0-en-105":90},{"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":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":20},122883,1099523885074,"Ivy","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","COVID-19 Fake News Detection Model on Social Media Data Using Machine Learning Techniques","Social media platforms accelerate the spread of news, images, and information, but the COVID-19 pandemic also triggered widespread disinformation that can mislead consumers and increase public concern. The work builds a dataset by synthesizing COVID-19-related news from multiple social media and news sources, extracting markers from unstructured text. Feature selection reduces computational cost, then several machine learning models are trained and evaluated using accuracy, precision, recall, and F1 score.","2023 IEEE 8th International Conference On Software Engineering and Computer Systems ( ICSECS) | 979-8-3503-1093-1/23/$31.00 ©2023 IEEE | DOI: 10. 1 109/ ICSECS58457. 2023. 10256386  \nCOVID-19 FAKE NEWS DETECTION MODEL ON SOCIAL MEDIA DATA USING MACHINE LEARNING TECHNIQUES  \nKelvin Liew Kai Xuan Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah Pahang, Malaysia [liewliew520@gmail.com](liewliew520@gmail.com)  \nMohaiminul Islam Bhuiyan Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah Pahang, Malaysia [rafe.aust@gmail.com](rafe.aust@gmail.com)  \nNur Shazwani Kamarudin∗  \nFaculty of Computing Universiti Malaysia Pahang AlSultan Abdullah Pahang, Malaysia[nshazwani@ump.edu.my](nshazwani@ump.edu.my)  \nAhmad Fakhri Ab. Nasir Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah Pahang, Malaysia [afakhri@ump.edu.my](afakhri@ump.edu.my)  \nMuhammad Zulfahmi Toh Abdullah Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah  \nPahang, Malaysia [zulfahmi@ump.edu.my](zulfahmi@ump.edu.my)  \nAbstract—Social media sites like Instagram, Twitter, and Facebook have become indispensable parts of the daily routine. These social media sites are powerful instruments for spreading the news, photographs, and other sorts of information. However, since the emergence of the COVID-19 pandemic in December 2019, many articles and headlines concerning the COVID-19 epidemic have surfaced on social media. Social media is frequently used to disseminate fraudulent material or information. This disinformation may confuse consumers, perhaps causing worry. It is hard to counter the widespread dissemination of disinformation. As a result, it is critical to develop a model for recognizing fake news in the news stream. The dataset, which would be a synthesis of COVID-19-related news from numerous social media and news sources, is utilized for categorization in this work. Markers are retrieved from unstructured textual data gathered from a variety of sources. Then, to eliminate the computational burden of analyzing all of the features in the dataset, featureselection is done. Finally, to categorize the COVID -19 related dataset, multiple cutting-edge machine-learning algorithms were trained. Support Vector Machine (SVM), Nave Bayes (NB), and Decision Tree (DT) are the machine learning models presented. Finally, numerous measures are used to evaluate these algorithms such as accuracy, precision, recall, and F1 score. The Decision Tress algorithm reported the highest accuracy of 100% compared to the Support Vector Machine 98.7% and Nave Bayes 96.3% . Keywords—fake news, social media, machine learning  \nI. INTRODUCTION  \nSocial media platforms like Facebook, Twitter, Instagram, and others have risen in the twenty-first century, allowing information to travel swiftly. Users on social media can publish whatever they wish, regardless of the provenance and credibility of the published material, posing problems to information dependability assurance. Each social media user  \ncould have as many accounts as they wish. With the COVID- 19 epidemic, millions of posts or news are being sent out every day, with some detrimental repercussions for people and society. For example, the propagation of false information concerning COVID-19 patient data or symptoms that have yet to be confirmed. People might quickly become panicked as a result of social media’s fake news and disinformation. The phrases fake news and disinformation are closely related and are sometimes used interchangeably. An automated false news detection system is required, which will rely on human annotation, machine learning, and natural language processing [1] . Since its emergence in December 2019, there have been several articles and media articles on the COVID-19 outbreak on the internet, traditional print, and digital media. These sources provide data from both credible and untrustworthy clinical sources. Furthermore, news from these outlets spreads swiftly.","cbCaiqUBrr2iq0Nn","https://ap.wps.com/l/cbCaiqUBrr2iq0Nn","pdf",160716,3,1,"English","en",105,"# Abstract\n# Introduction\n# Dataset and Feature Selection\n# Machine Learning Models and Evaluation","[{\"question\":\"How is the COVID-19 dataset for fake news detection constructed?\",\"answer\":\"The dataset synthesizes COVID-19-related news from multiple social media and news sources and derives markers from unstructured textual data.\"},{\"question\":\"Why is feature selection included in the model pipeline?\",\"answer\":\"Feature selection is used to eliminate the computational burden of analyzing all features in the dataset.\"},{\"question\":\"Which machine learning algorithms are trained and how are they evaluated?\",\"answer\":\"Support Vector Machine, Nave Bayes, and Decision Tree are trained and evaluated using accuracy, precision, recall, and F1 score.\"}]","COVID-19 Fake News Detection Model on Social Media Data Using Machine Learning Techniques | PDF",1785813492,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":28},"covid-19-fake-news-detection-model-on-social-media-data-using-machine-learning-techniques","",{"@graph":35,"@context":84},[36,52,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,49],{"item":40,"name":41,"@type":42,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":20},"https://docshare.wps.com/document/research-report/",{"item":50,"name":13,"@type":42,"position":51},"https://docshare.wps.com/document/covid-19-fake-news-detection-model-on-social-media-data-using-machine-learning-techniques/122883/",4,{"url":50,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"inLanguage":23,"description":14,"dateModified":60,"datePublished":61,"encodingFormat":59,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":40,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-09-11","2026-08-04",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"How is the COVID-19 dataset for fake news detection constructed?","Question",{"text":74,"@type":75},"The dataset synthesizes COVID-19-related news from multiple social media and news sources and derives markers from unstructured textual data.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Why is feature selection included in the model pipeline?",{"text":79,"@type":75},"Feature selection is used to eliminate the computational burden of analyzing all features in the dataset.",{"name":81,"@type":72,"acceptedAnswer":82},"Which machine learning algorithms are trained and how are they evaluated?",{"text":83,"@type":75},"Support Vector Machine, Nave Bayes, and Decision Tree are trained and evaluated using accuracy, precision, recall, and F1 score.","https://schema.org",{"og:url":50,"og:type":86,"og:title":13,"og:site_name":57,"og:description":14},"article",{"robots":88,"canonical":50},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,127,130,134],{"id":21,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":51,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":45,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":45,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":45,"category_name":136,"show_sort_weight":105,"slug":137},19,"General","general"]