[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127123-en":3,"doc-seo-127123-105":30,"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":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},127123,687207022233,"Riley","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine learning-based detection of fake news in Afan Oromo language","Machine learning approach for identifying fake news on web-based social media networks, using data acquired from Facebook to model Afan Oromo false news detection. A system architecture applies SVM, KNN, and convolutional neural networks (CNNs) for detection and classification, addressing shortcomings of prior models that inadequately compare reported accuracy with verified news. The study leverages natural language processing (NLP) techniques and demonstrates strong performance, with SVM achieving precision 0.92, recall 0.92, and F1-score 0.90.","Machine learning-based detection of fake news in Afan Oromo  \nlanguage  \nAyodeji Olalekan Salau1,3,7, Kedir Lemma Arega2,8, Ting Tin Tin3, Andrew Quansah4, Kwame SefaBoateng5, Ismatul Jannat Chowdhury4, Sepiribo Lucky Braide6  \n1Department of Electrical/Electronics and Computer Engineering, Afe Babalola University , Ado-Ekiti, Nigeria 2Department of Information Technology, School of Technology and Informatics, Ambo University, Oromia, Ethiopia 3Faculty of Data Science and Information Technology, INTI International University, Nilai, Malaysia 4Department of Electrical and Computer Engineering, University of North Carolina, Charlotte, USA 5College of Computing and Informatics, University of North Carolina at Charlotte, North Carolina, USA 6Department of Electrical and Electronics Engineering, Rivers State University, Port Harcourt, Nigeria 7Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India 8Department of Software Engineering, Addis Ababa Science and Technology University, Addis Ababa, Ethiopia  \n\n| Article history:\u003Cbr>Received Dec 11, 2023 Revised May 4, 2024 Accepted May 17, 2024 |\n| --- |\n| Keywords:\u003Cbr>Afan Oromo Classification Detection Ethnic conflict Fake news Machine learning |\n\nCorresponding Author:  \nThis paper presents a machine learning-based (ML) approach for identifying fake news on web-based social media networks. Data was acquired from Facebook to develop the model which was used to identify Afan Oromo's false news. The system architecture uses algorithms, such as support vector machines (SVM), k-nearest neighbor (KNN), and convolutional neural networks (CNNs) to detect and classify fake news. Existing models have limitations in understanding reported news accuracy compared with verified news. This study successfully resolved the challenges in the detection of social media fake news detection for the Afan Oromo language with the use of ML models and natural language processing (NLP) techniques. The results show that the SVM approach achieved a precision, recall, and F1-score, of 0 .92, 0.92, and 0.90.  \nThis is an open access article under the CC BY-SA license.  \nAyodeji Olalekan Salau  \nDepartment of Electrical/Electronics and Computer Engineering, Afe Babalola University Ado-Ekiti, Nigeria  \nEmail: [ayodejisalau98@gmail.com](ayodejisalau98@gmail.com)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nOnline social networks are a popular means of communication. Numerous persons engage in interpersonal interactions and disseminate information daily through visual content as well as post updates on their present status on social media platforms. Many individuals possess a fondness for employing social networking platforms, such as Facebook because of the rapidity with which information can be shared and friendly user interface [1] . With the development of communication technologies and social media, fake news has rapidly expanded. Fake news identification is a recent research area that has gained a lot of interest [2] . Fake news is a major issue in today's social media and political landscape. The detection of fake news necessitates extensive study, although there are several challenges [3] . Misinformation is a serious problem involving various individuals that disseminate content that might be truthful or damaging. The spread of false news is a deliberate act, and the use of online social media networks by the users presents unique challenges. Traditional news sources' identification algorithms are inadequate, but people seek news from social networks due to their ease of access, minimal effort, and rapid dissemination [4], [5] . Social media is a popular medium for individuals to share their thoughts, views, and news. Afan Oromo, Africa's  \nlargest language, is widely spoken in Ethiopia and neighboring nations. It provides fiction, literature, and journalism, as well as the ability for spectators and witnesses to speak about occurrences. This media has a tremendous i","cbCaimxlPeN3PR5o","https://ap.wps.com/l/cbCaimxlPeN3PR5o","pdf",630187,1,13,"English","en",105,"# Keywords\n## Afan Oromo classification detection\n# 1. Introduction\n## Online social networks and fake news\n## Challenges and research motivation","[{\"question\":\"What is the main goal of this study?\",\"answer\":\"To detect and classify fake news in Afan Oromo language using machine learning and NLP techniques on social media content.\"},{\"question\":\"Which algorithms does the proposed system use?\",\"answer\":\"Support vector machines (SVM), k-nearest neighbor (KNN), and convolutional neural networks (CNNs) are used for detection and classification.\"},{\"question\":\"Where does the study obtain its data?\",\"answer\":\"The dataset is acquired from Facebook to train and develop the model for Afan Oromo false news identification.\"}]","Machine learning-based detection of fake news in Afan Oromo language | PDF",1785936963,33,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"machine-learning-based-detection-of-fake-news-in-afan-oromo-language","",{"@graph":36,"@context":86},[37,54,69],{"@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/machine-learning-based-detection-of-fake-news-in-afan-oromo-language/127123/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"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 is the main goal of this study?","Question",{"text":76,"@type":77},"To detect and classify fake news in Afan Oromo language using machine learning and NLP techniques on social media content.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which algorithms does the proposed system use?",{"text":81,"@type":77},"Support vector machines (SVM), k-nearest neighbor (KNN), and convolutional neural networks (CNNs) are used for detection and classification.",{"name":83,"@type":74,"acceptedAnswer":84},"Where does the study obtain its data?",{"text":85,"@type":77},"The dataset is acquired from Facebook to train and develop the model for Afan Oromo false news identification.","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":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]