[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121813-en":3,"doc-seo-121813-105":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":4,"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},121813,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Fake News Detection Using Machine Learning - An Exhaustive Review","Fake news can significantly affect elections and daily life by spreading misleading information. This review outlines a machine-learning pipeline to analyze large-scale data and detect false or deceptive content through steps including data collection, preprocessing, feature extraction, model training, evaluation, and deployment. It highlights limitations in existing web-based approaches that rely on predefined datasets or keyword matching, and argues for models that incorporate historical and current datasets plus contextual information to improve distinction between genuine and counterfeit news.","Fake News Detection Using Machine Learning: An Exhaustive Review  \nArisha Farha 1 and Afsaruddin2  \n1PG Student, Department of Computer Science & Engineering, Integral University, Lucknow, U.P. , INDIA 2Assistant Professor, Department of Computer Science & Engineering, Integral University, Lucknow, U.P. , INDIA  \n1Corresponding Author: [arishaaz@student.iul.ac.in](arishaaz@student.iul.ac.in)  \nReceived: 11-03-2023 Revised: 27-03-2023 Accepted: 27-04-2023  \nABSTRACT  \nFake news can have serious consequences, from influencing elections to spreading harmful misinformation. Machine learning can be used to help combat the spread of fake news by analyzing large amounts of data and identifying patterns that may indicate the presence of false or misleading information. Here are the steps that can be taken to perform fake news analysis using machine learning. Data Collection, Data Preprocessing, Feature Extraction, Model Training, Model Evaluation, and Model Deployment. That is the reason today we need a PC fake wise based model that can identify any phony news before it is posted. All web-based media stages have worked towards this path, however, in some places it appears to be that their model is deficient to catch such phony news. Since some web-based media organizations have attempted to choose whether the news is phony or not based on some predefined datasets. Furthermore, a few organizations have looked through just the watchwords of the news that the news is phony. This demonstrates that we need a model that depends on the old dataset, and the current news dataset and watchwords. Alongside this, focus on the circumstance, spot, and kind of information, while these things are not dealt with in the current models. So I might want to remember this load of boundaries for my model to assist with distinguishing counterfeit news. On the off chance that we perceive Fake News as the ideal opportunity, we can make the perfect strides at the perfect time. PC based models are not generally exact, so the model ought to likewise have the office to contrast and genuine news. Assuming news is contrasted and current information, 76% of phony news can be distinguished simultaneously. Accordingly, the model ought to likewise have the office of the relative survey  \nKeywords— Decision Tree Algorithm, Real News, Fake News, Genuine  \nI. INTRODUCTION  \nIn today's time, 70% of the world has expressedits presence in the virtual world, which means that more than half the world is connected to this virtual world i.e. Internet world in some way or the other. In earlier times, people did not have any open means where they could openly put their ideas in front of the world. Where he can  \ntalk about himself, about his society, or his religion and customs. Social media is such a platform in today's time where people can share their problems and get tips to get out of them. By using social media today, people can also raise their voices against the injustice done to them and get public support. In today's time, the governments of countries have started using social media, they are taking their agenda very easily to the people. Political parties are using social media to express their views. Through social media, the work done by him and his party to reach the public so that they can take advantage of this in the elections. Many times people also use social media to bring out someone's talent as we have seen. This changes that person's life overnight. But as we know where there is light there is room for darkness too. And sometimes freedom also brings with it arrogance and people also take wrong advantage of this freedom. What I mean to say is that people use social media to tell about themselves, society, religion, or customs. But sometimes some wrong people start taking advantage of this to spread their wrong feelings which is wrong. Anomaly detection in modern networks is complex because there are so many different kinds of networks, each with unique propert","cbCaip0jcQ4B7EA1","https://ap.wps.com/l/cbCaip0jcQ4B7EA1","pdf",364623,1,5,"English","en",105,"# Introduction\n# Related Concepts and Motivation\n# Proposed Machine Learning Workflow\n## Data Collection and Preprocessing\n## Feature Extraction and Model Training\n## Model Evaluation and Deployment","[{\"question\":\"What problem does fake news detection address?\",\"answer\":\"It addresses the spread of false or misleading information that can influence elections and harm people by provoking harmful reactions.\"},{\"question\":\"What are the main steps in the machine-learning workflow described?\",\"answer\":\"The document outlines data collection, data preprocessing, feature extraction, model training, model evaluation, and model deployment.\"},{\"question\":\"Why are current models sometimes insufficient?\",\"answer\":\"Some web-based media approaches depend on predefined datasets or only keyword/watchword checks, which may miss context and other signals needed to catch fake news.\"}]","Fake News Detection Using Machine Learning - 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