[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124215-en":3,"doc-seo-124215-105":29,"detail-sidebar-cat-0-en-105":90},{"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":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},124215,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Real Time Verification of Fake News using Machine Learning Algorithms and Natural Language","The rise of fake news on digital platforms threatens public opinion and weakens trust in legitimate news sources as misinformation spreads quickly and blurs the line between fact and fiction. Harmful impacts extend across politics, public health, and social relationships, motivating the need for advanced detection and classification systems. The project develops an automated machine learning pipeline using NLP to identify deceptive textual information with high precision. A key step includes Part-of-Speech tagging to capture subtle linguistic patterns, preserving meaning for subsequent modeling and enabling accurate real-time verification through transformer-based language modeling.","International Journal of Innovative Research in Science  \nEngineering and Technology (IJIRSET)  \n(A Monthly, Peer Reviewed, Refereed, Scholarly Indexed, Open Access Journal)  \nImpact Factor: 8.699 Volume 14, Issue 4 , April 2025  \n|[www.ijirset.com](www.ijirset.com |A Monthly)[ |A Monthly](www.ijirset.com |A Monthly), Peer Reviewed & Refereed Journal| e-ISSN: 2319-8753| p-ISSN: 2347-6710|  \nVolume 14, Issue 4, April 2025  \n|DOI: 10.15680/IJIRSET.2025.1404418|  \nReal Time Verification of Fake News using Machine Learning Algorithms and Natural Language  \nK Muthulakshmi, M Raghava Manikumar, M Chaitanya Bharathi,  \nAssistant Professor, Department ofCSE, Bharath Institute of Higher Education and Research, Chennai, India  \nB.Tech Student, Department ofCSE, Bharath Institute of Higher Education and Research, Chennai, India  \nB.Tech Student, Department ofCSE, Bharath Institute of Higher Education and Research, Chennai, India  \nABSTRACT: The rise of fake news on digital platforms has become a major societal issue, shaping public opinion and eroding trust in legitimate news sources. As misinformation spreads rapidly, it becomes increasingly difficult to discern fact from fiction, leading to harmful consequences in various domains such as politics, public health, and social relationships. To combat this, there is a growing need for advanced systems that can accurately detect and classify fake news. This project aims to develop a robust machine learning system, leveraging Natural Language Processing (NLP) techniques and machine learning algorithms, to identify deceptive information in textual data with high precision. By focusing on automation, this system promises to scale effectively and provide a timely solution to the growing problem of fake news. The proposed solution incorporates a pre-processing phase that includes Part-of-Speech (POS) tagging, a technique used to analyze the grammatical structure of the text. POS tagging helps in understanding the syntactic roles of words, which can significantly enhance the ability of the model to detect subtle linguistic patterns often associated with misinformation. The system will align these POS-tagged texts with their original content, ensuring that the underlying meaning is preserved while enabling further analysis. This pre-processing is a crucial step before integrating advanced models, such as General Pre-trained Transformers (GPT), which are known for their strong language modeling capabilities. The combination of these techniques will ensure the model achieves a high level of accuracy in detecting fake news.  \nKEYWORDS: Machine Learning, NLP, Artificial Intelligence.  \nI. INTRODUCTION  \nThe proliferation of fake news on digital platforms poses a growing threat to society by influencing public opinion and undermining trust in legitimate sources of information. Misinformation can have harmful consequences, especially in areas such as politics, health, and social relations. By developing a machine learning system to detect and classify fake news, this project aims to enhance the integrity of public information consumption. The system will help users distinguish between fact and fiction, thereby promoting more informed decision-making. This tool is designed to support various sectors in combating misinformation, offering a scalable solution to address the challenges posed by fake news. In the long term, it can contribute to restoring trust in media outlets and ensuring that only credible information reaches the public. By improving the ability to verify news content, the project has the potential to create a more transparent and reliable information ecosystem. Ultimately, it seeks to combat misinformation and reinforce societal trust in the digital age.  \nMachine learning plays a pivotal role in automating the process of fake news detection by analyzing large sets of textual data for patterns indicative of misinformation. By utilizing advanced machine learning algorithms and Natural Langu","cbCaiq3kH7Am3Ncv","https://ap.wps.com/l/cbCaiq3kH7Am3Ncv","pdf",1222170,1,"English","en",105,"# Abstract\n# Keywords\n# Introduction\n# System Model","[{\"question\":\"What problem does the project address?\",\"answer\":\"The project targets the rapid spread of fake news on digital platforms and the difficulty of distinguishing fact from fiction. It aims to reduce harmful consequences across multiple social domains.\"},{\"question\":\"How does the proposed system detect fake news?\",\"answer\":\"It uses NLP and machine learning to learn linguistic and contextual patterns from textual data. The approach includes a pre-processing phase such as Part-of-Speech tagging to enhance pattern detection.\"},{\"question\":\"What is the role of POS tagging and transformer models?\",\"answer\":\"POS tagging analyzes grammatical roles of words to support identification of subtle linguistic cues linked to misinformation. The processed texts are then used with advanced models, including General Pre-trained Transformers (GPT), to improve classification accuracy.\"}]","Real Time Verification of Fake News using Machine Learning Algorithms and Natural Language | PDF",1785821062,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"real-time-verification-of-fake-news-using-machine-learning-algorithms-and-natural-language","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/real-time-verification-of-fake-news-using-machine-learning-algorithms-and-natural-language/124215/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What problem does the project address?","Question",{"text":74,"@type":75},"The project targets the rapid spread of fake news on digital platforms and the difficulty of distinguishing fact from fiction. It aims to reduce harmful consequences across multiple social domains.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does the proposed system detect fake news?",{"text":79,"@type":75},"It uses NLP and machine learning to learn linguistic and contextual patterns from textual data. The approach includes a pre-processing phase such as Part-of-Speech tagging to enhance pattern detection.",{"name":81,"@type":72,"acceptedAnswer":82},"What is the role of POS tagging and transformer models?",{"text":83,"@type":75},"POS tagging analyzes grammatical roles of words to support identification of subtle linguistic cues linked to misinformation. The processed texts are then used with advanced models, including General Pre-trained Transformers (GPT), to improve classification accuracy.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":28,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":28,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":28,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]