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Feature-importance analysis highlights strong indicators such as “unverified” and “anonymous sources,” supporting practical digital-age mitigation of misinformation.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/an-implementation-of-machine-learning-algorithm-for-fake-news-detection/290449/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/an-implementation-of-machine-learning-algorithm-for-fake-news-detection/290449.png","ImageObject",300,407,{"name":42,"@type":43},"Hazel","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-11-04","2026-09-17",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",13,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What models does the paper propose for fake news detection?","Question",{"text":63,"@type":64},"The paper proposes two models: LSTM and SVM, using them to classify news as fake or real.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How did the models perform in experiments?",{"text":68,"@type":64},"Experiments on a news-article dataset show high accuracy for both models; LSTM achieved 94% compared with 89% for SVM.",{"name":70,"@type":61,"acceptedAnswer":71},"Which text features are identified as important for detecting fake news?",{"text":72,"@type":64},"Feature importance analysis indicates that certain words and phrases, such as “unverified” and “anonymous sources,” are strong indicators of fake news.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},290449,1789641715,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,111,115,120,123,127],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":25,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":25,"category_name":108,"show_sort_weight":109,"slug":110},7,"Healthcare",40,"healthcare",{"id":112,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":113,"slug":114},8,30,"research-report",{"id":116,"doc_module":4,"doc_module_name":25,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":25,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":124,"doc_module":4,"doc_module_name":25,"category_name":125,"show_sort_weight":124,"slug":126},10,"Lifestyle","lifestyle",{"id":128,"doc_module":4,"doc_module_name":25,"category_name":129,"show_sort_weight":97,"slug":130},19,"General","general",{"code":4,"msg":82,"data":132},{"doc_id":79,"user_id":133,"nickname":42,"user_avatar":134,"doc_module":4,"category_id":112,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":124,"language":140,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":12,"update_tm":80,"read_time":144,"preview_page_count":4},137441390410,"https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984","An Implementation of Machine Learning Algorithm  \nfor Fake News Detection  \nSudhir Bussa1, Aniruddha Bodhankar2, Dr. Vinod H Patil3, Dr. Hemant Pal4, Dr. Satyendra Kumar Bunkar5, Dr.  \nAbdul Razzak Khan Qureshi6  \n1Department of E&TC Engineering, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune, India [Email:ssbusa@bvucoep.edu.in](Email:ssbusa@bvucoep.edu.in),  \n2Department of Business Analytics, Dr Ambedkar Institute of Management Studies and Research, Nagpur [Email:aniruddha.bodhankar16@gmail.com](Email:aniruddha.bodhankar16@gmail.com),  \n3Department of E&TC Engineering, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune, India Email:[vhpatil@bvucoep.edu.in](vhpatil@bvucoep.edu.in),  \n4Department of Computer Science, Medi-Caps University, Indore [Email:hemantpal.scs@gmail.com](Email:hemantpal.scs@gmail.com)  \n5Department of Computer Science and Engineering, Chameli Devi Group of Institutions, Indore [Email:](Email:satyendra.bunkar@gmail.com)[s](Email:satyendra.bunkar@gmail.com)[atyendra.bunkar@gmail.com](Email:satyendra.bunkar@gmail.com)  \n6Department of Computer Science, Medi-Caps University, Indore [Email:dr.arqureshi786@gmail.com](Email:dr.arqureshi786@gmail.com)  \nAbstract—Fake news is a growing concern in the age of social media, as it can spread rapidly and have serious consequences. To combat this issue, machine learning techniques have been used for fake news detection. In this study, we propose two models, LSTM and SVM, for fake news detection. The LSTM model is a deep learning algorithm that is particularly suited to sequential data such as text. It can capture longterm dependencies in the text and has shown promising results in natural language processing tasks. The SVM model, on the other hand, is a classical machine learning algorithm that has been widely used for classification tasks. To evaluate the performance of the proposed models, we conducted experiments on a dataset of news articles. Our results show that both models achieve high accuracy in detecting fake news. However, the LSTM model outperforms the SVM model with an accuracy of 94% compared to 89% . Furthermore, we conducted a feature importance analysis to determine the most important features for detecting fake news. The results show that the presence of certain words and phrases, such as \"unverified\" and \"anonymous sources\", are strong indicators of fake news. In conclusion, our study demonstrates the effectiveness of using machine learning techniques, particularly LSTM and SVM, for detecting fake news. This research can be applied to assist individuals and organizations in identifying and combating fake news in the digital age.  \nKeywords-LSTM, SVM, Word to vector, Embedding, RNN.  \nI. INTRODUCTION  \nIn recent years, the issue of fake news has become a significant concern, especially in the era of social media and digital communication. Fake news is a type of misleading information that is intentionally created to deceive readers, distort facts, or spread propaganda. The spread of fake news can cause serious consequences, including social polarization, public panic, and the erosion of trust in the media. Therefore, it is essential to develop effective methods to detect and combat fake news [1]  \nThe last few decades have witnessed technology take over our life. It has altered how we converse and exchange information. Physical barriers are no longer a barrier to information sharing. Transmitting information in the form of text, audio, and video is simple on a global scale. The social media platforms are a crucial component of this capacity. These platforms make sharing information and individual perspectives  \neasier with a much larger audience. Because of their speed and specialized information, they have displaced traditional media outlets. However, spreading false information on social media platforms has also become very simple for evildoers with ulterior motives.[2]  \nIn their current condition, ","cbCaiphTAcSHWjKU","https://ap.wps.com/l/cbCaiphTAcSHWjKU","pdf",354042,"English","# Abstract\n# Keywords\n# I. INTRODUCTION","[{\"question\":\"What models does the paper propose for fake news detection?\",\"answer\":\"The paper proposes two models: LSTM and SVM, using them to classify news as fake or real.\"},{\"question\":\"How did the models perform in experiments?\",\"answer\":\"Experiments on a news-article dataset show high accuracy for both models; LSTM achieved 94% compared with 89% for SVM.\"},{\"question\":\"Which text features are identified as important for detecting fake news?\",\"answer\":\"Feature importance analysis indicates that certain words and phrases, such as “unverified” and “anonymous sources,” are strong indicators of fake news.\"}]","An Implementation of Machine Learning Algorithm for Fake News Detection | PDF",25]