[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125784-en":3,"doc-seo-125784-105":30,"detail-sidebar-cat-0-en-105":91},{"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},125784,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Applying Ensemble Machine Learning Techniques for Fake News Identification","Social media has accelerated the spread of information, increasing the volume of inaccurate and non-factual content that users generate and disseminate. Detecting false or misleading statements in text remains a major challenge requiring domain-aware factors before judging article veracity. This paper presents an ensemble-based approach that leverages complementary textual features from authentic and fake news. Using a dataset of 72,134 news articles and multiple classifiers, experiments show the ensemble model consistently outperforms individual learners in key metrics.","Applying Ensemble Machine Learning Techniques  \nfor Fake News Identification  \nVipul Devendra Punjabi 1, 2 *, Dr. Rajesh Kumar Shukla 1, Dr. B. V. Kiranmayee 3  \nResearch Scholar, CSE Department,  \n1 Oriental University, Indore, MP, India.  \n2 R. C. Patel Institute of Technology, Shirpur, MH, India.  \n0000-0003-4221-7657,  \n* [vipulchaddha@gmail.com](vipulchaddha@gmail.com)  \n1 Oriental University, Indore, MP, India.  \n0000-0002-6602-0229  \n3 VNRVJIET, Hydrabad, India  \nAbstract-The sharing of information has entered an unprecedented era in human history due to emergence of the Internet With the widespread use of social media sites like Facebook and Twitter. As these platforms enjoy extensive use, users are generating and disseminating a wealth of information, some of which is inaccurate and devoid of factual basis. Detecting false or misleading information within textual content poses a significant challenge. Before arriving at a judgment regarding the accuracy of an article, it is imperative to consider various factors within a specific domain. This paper proposes an Ensemble method for the identification of fraudulent news stories. We leverage different textual features found in both authentic and fake news articles. Our dataset comprises 72,134 news articles, with 35,028 being genuine and 37,106 being false, categorized as binary 0s and 1s. To evaluate our approach, we employed well-known machine learning classifiers including Logistic Regression (LR), Decision Tree, AdaBoost, XGBoost, Random Forest, Extra Trees, SGD, SVM, and Naive Bayes.  \nTo enhance the precision of our findings, we devised a multi-model system for identifying fake news the Ensemble approach and the aforementioned classifiers. Experimental analysis conclusively demonstrates that our suggested ensemble learning technique surpasses the performance of individual learners.  \nKeywords-Fake news, Bogus news, false information, Ensemble, Social media, Web, Machine learning.  \n1. INTRODUCTION  \nFake news, which involves spreading erroneous material that is disguised as actual news is a pervasive issue in contemporary society, despite its historical presence. Various methods, such as machine learning and linguistic analysis, are used. Knowledgebased approaches, topic-agnostic strategies, and hybrid techniques, have been proposed to identify deceptive news sources [1, 10] . Zhou, [X. et](X. et) al. have outlined four criteria for recognizing false news, including the volume of misinformation, dissemination patterns, writing style, and source reliability [11] . Numerous studies by researchers have aimed to identify fake news, misinformation, disinformation, and detection  \nmethodologies [3, 4, 5, 16, 17, 18, 19, 20] .  \nIn this study, we introduce an ensemble technique for identifying fake news articles by leveraging distinct textual characteristics that separate authentic from false news. We utilized a publicly available dataset comprising 20,800 news articles, with 10,387 classified as true and 10,413 as false, represented by binary labels (0s and 1s) . To evaluate our approach, we employed several widely used Decision trees and logistic regression are two examples of machine learning classifiers. XGBoost (XGB), Extra Trees (ET), Naive Bayes (NB),(SGD) Stochastic Gradient Descent, Random Forest (RF),  \nAdaBoost (AB), Support Vector Machine (SVM) . We constructed a multi-model system by integrating such classifiers with the ensembles technique, it is possible to detect fake news with greater accuracy. Our experimental results show that our suggested technique was successful. Impressive results, with precision and accuracy reaching 97% and 96.57%, respectively, along with a 97% recall and 97% F1-measure. Notably, the ensemble method outperformed individual learning methods. The following is a summary of the study's major contributions: Introducing an ensemble a method-based approach for identifying fake news, demonstrating superior performance compared to individu","cbCaih9DjKVSY5VD","https://ap.wps.com/l/cbCaih9DjKVSY5VD","pdf",478012,1,10,"English","en",105,"# Introduction\n# Related Work","[{\"question\":\"What problem does the document address?\",\"answer\":\"It addresses the challenge of identifying fraudulent or misleading news articles within textual content, especially as social media expands the spread of misinformation.\"},{\"question\":\"What approach does the paper propose?\",\"answer\":\"The paper proposes an ensemble machine learning method that combines multiple classifiers and leverages diverse textual features from genuine and fake news.\"},{\"question\":\"How are the models evaluated and what results are reported?\",\"answer\":\"The document evaluates the approach using well-known classifiers and reports that the ensemble technique outperforms individual models, achieving around 97% precision and 96.57% accuracy, with strong recall and F1-measure.\"}]","Applying Ensemble Machine Learning Techniques for Fake News Identification | 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problem does the document address?","Question",{"text":75,"@type":76},"It addresses the challenge of identifying fraudulent or misleading news articles within textual content, especially as social media expands the spread of misinformation.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What approach does the paper propose?",{"text":80,"@type":76},"The paper proposes an ensemble machine learning method that combines multiple classifiers and leverages diverse textual features from genuine and fake news.",{"name":82,"@type":73,"acceptedAnswer":83},"How are the models evaluated and what results are reported?",{"text":84,"@type":76},"The document evaluates the approach using well-known classifiers and reports that the ensemble technique outperforms individual models, achieving around 97% precision and 96.57% accuracy, with strong recall and 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