[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128678-en":3,"doc-seo-128678-105":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},128678,962084928432,"Emma Wilson","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Fake News Detection Model Basing on Machine Learning Algorithms - Fake News Detection","The rapid growth of the internet and the ease of communication enable fast creation and dissemination of news, while social media users often generate and share content that can be false or disconnected from reality. Detecting fake information in text remains difficult because authenticity requires considering multiple textual factors. This study builds a machine learning model that analyzes text characteristics, converts words into TF-IDF features, selects top features, and classifies news as real or fake using Logistic Regression, Decision Tree, Gradient Boosting, and Random Forest.","Fake News Detection Model Basing on Machine Learning Algorithms  \nMohammed A. Taha*1, Haider D. A.Jabar2 , Widad K. Mohammed2   \n1Ministry of Education, Babylon Education Directorates, Babylon, Iraq.  \n2Ministry of Education, Baghdad, Iraq.  \n*Corresponding Author.  \nReceived 07/03/2023, Revised 14/07/2023, Accepted 16/07/2023, Published Online First 20/01/2024, Published 01/08/2024  \n © 2022 The Author(s) . Published by College of Science for Women, University of Baghdad.  \nThis is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is  \nproperly cited.   \nAbstract  \nThe rapid growth of the internet and easy communication has made it quick and simple to create and spread news. Social media users now generate and share more information than before, but some of it is false and unrelated to reality. Detecting false information in text is challenging, even for experts who need to consider multiple factors to determine authenticity. Malicious misinformation on social media negatively affects societies, especially during crises like terrorist attacks, riots, and natural disasters. To minimize the harmful impact, it is crucial to identify rumors quickly. This study aims to build a learning model for detecting fake news. This research paper relies on finding and analyzing the characteristics of the text, then the words are converted into features using TF-IDF technology, after that the highest-ranking features are identified for the purpose of studying and distinguishing the spread of news, whether it is real or fake using machine learning techniques. Finally, the Logistic Regression, Decision Tree, Gradient Boosting and Random Forest algorithm has been adapted. The accuracy of Logistic Regression is 0.985, Random Forest (0.989) whereas the accuracy of Decision Tree is 0.994 and Gradient Boosting (0.9949), respectively.  \nKeywords: Classification, Decision Tree, Gradient Boosting, Logistic Regression, Random Forest.  \nIntroduction  \nThe internet and communication technologies have revolutionized communication, making it faster and more accessible. Popular social networks like Facebook have played a key role in spreading news rapidly, replacing traditional print media. For example, Facebook referral traffic accounts for 70% of all traffic to news websites 1. In the era of fast and efficient information flow through online platforms, people are susceptible to deception and manipulation, leading to lasting consequences. The popularity of social networking websites has enabled global information sharing and  \nconsumption, making news readily accessible. The way that information is disseminated around the world and on the web has changed recently as a result of web-based media2. The mass distribution of false information has detrimental effects on people and society. It disrupts social authenticity, promotes biased opinions, particularly through political propaganda, and hampers the comprehension and response to real news 3.Text categorization is the technique of organizing and classifying texts based on their content. It plays a crucial role in natural language processing (NLP),  \nespecially in tasks like subject labelling, spam detection, and sentiment analysis. NLP enables the automatic detection of relevant medical research documents, journals, and other sources worldwide by selecting specific labels or categories. However, evaluating the similarity of training dataset inputs remains important even after categorization. By employing NLP, machine learning, and data mining, patterns in electronic texts are automatically discovered and uncovered 4. The technology's main goal is to make it possible for people to handle tasks involving text mining and information extraction from textual tools. Technologies for information extraction (IE) aim to extract exact information from ","cbCairoFIAOjLn3d","https://ap.wps.com/l/cbCairoFIAOjLn3d","pdf",932001,1,11,"English","en",105,"# Introduction\n## Background and Challenges of Fake News\n## Text Categorization and NLP Role\n## Prior Approaches in Fake News Detection","[{\"question\":\"What problem does the study address?\",\"answer\":\"The study addresses the challenge of detecting false information in text that spreads through online and social media channels, including during major crises.\"},{\"question\":\"How are text features created for the model?\",\"answer\":\"Words are converted into features using TF-IDF, and the highest-ranking features are selected for distinguishing real versus fake news.\"},{\"question\":\"Which machine learning algorithms are used and what performance is reported?\",\"answer\":\"The study adapts Logistic Regression, Decision Tree, Gradient Boosting, and Random Forest, reporting accuracies around 0.985 to 0.9949 depending on the algorithm.\"}]","Fake News Detection Model Basing on Machine Learning Algorithms - 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