[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123284-en":3,"doc-seo-123284-105":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},123284,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Fake News Detection - Leveraging Natural Language Processing and Machine Learning for Reliable Information Verification","This thesis develops and evaluates a machine learning algorithm for fake news identification using Natural Language Processing (NLP). Multiple models are trained and tested, including Long Short-Term Memory (LSTM) and other deep learning approaches, to detect and classify misleading content. A multi-source dataset is compiled from social media and online news outlets, then cleaned and transformed through preprocessing steps such as tokenization, stop-word removal, and lemmatization. Model performance is measured with accuracy, precision, recall, and F1-score, with results showing LSTM outperforming traditional methods and hybrid models improving classification reliability. The work supports AI-driven misinformation defense, particularly for political contexts, and enables real-time social media filtering.","Rochester Institute of Technology  \nRIT Digital Institutional Repository  \nTheses  \n5-2025  \nFake News Detection: Leveraging Natural Language Processing and Machine Learning for Reliable Information Verification  \nRoshni Rajendra Salve[rs1625@rit.edu](rs1625@rit.edu)  \nFollow this and additional works at: [https://repository.rit.edu/theses](https://repository.rit.edu/theses)  \nRecommended Citation  \nSalve, Roshni Rajendra, \"Fake News Detection: Leveraging Natural Language Processing and Machine Learning for Reliable Information Verification\" (2025) . Thesis. Rochester Institute of Technology. Accessed from  \nThis Thesis is brought to you for free and open access by the RIT Libraries. For more information, please contact [repository@rit.edu](repository@rit.edu).  \nFake News Detection: Leveraging Natural Language Processing and Machine Learning for Reliable Information Verification  \nBy  \nRoshni Rajendra Salve  \nA Thesis Submitted in Partial Fulfilment of the Requirements for the Degree of Master of Science  \nin Professional Studies: Data Analytics  \nDepartment of Graduate Programs & Research  \nRochester Institute of Technology  \nRIT Dubai  \nRIT  \nMaster of Science in Professional Studies:  \nData Analytics  \nGraduate Thesis Approval  \nStudent Name: Roshni Rajendra Salve  \nThesis Title: Fake News Detection: Leveraging Natural Language Processing and Machine Learning for Reliable Information Verification.  \nGraduate Committee:  \nName: Dr. Sanjay Modak Date:  \nChair of committee  \n\n| Name: | Dr. Khalil Al Hussaeni Member of committee | Date: |\n| --- | --- | --- |\n\nAcknowledgments  \nI want to express my sincere and profound gratitude to my thesis supervisor, Dr. Khalil Al Hussaeni, for his invaluable support, insightful guidance, and constructive feedback that were instrumental throughout the entirety of this research journey. His expertise and his patient encouragement were invaluable in navigating the various complexities and challenges that arose during this project. His willingness to discuss ideas, offer direction, and provide timely feedback significantly contributed to the rigor and quality of this thesis. I am also deeply thankful to the esteemed members of my thesis committee for their thoughtful comments, pertinent questions, and valuable suggestions during both the proposal stage and the final review process. Their diverse perspectives and critical insights greatly enriched the scope and depth of this work. Their willingness to dedicate their time and expertise to my research is sincerely appreciated. Finally, I would like to acknowledge the Rochester Institute of Technology, Dubai branch, for providing a stimulating and supportive academic environment and the essential resources and infrastructure necessary to conduct this research effectively within the Master of Science in Data Analytics program. The opportunity to pursue this degree at this institution has been a truly enriching experience, and the knowledge and skills I have gained will undoubtedly be invaluable in my future endeavours. I eagerly anticipate sharing the culmination of this endeavor with you in the times ahead.  \nAbstract  \nThis dissertation details the creation and assessment of a machine learning algorithm designed to identify fake news utilizing Natural Language Processing (NLP) methods. The research employs several machines learning models, including Long Short-Term Memory (LSTM) and other deep learning techniques, to detect and classify misleading information. Data is sourced from a variety of platforms, such as social media and online news outlets, to compile a thorough dataset. The data is pre-processed to eliminate noise, address missing values, and extract essential features through techniques like tokenization, stop-word removal, and lemmatization. The performance of the models is evaluated using key metrics such as accuracy, precision, recall, and F1-score. The results indicate that LSTM models surpass traditional methods, offering more prec","cbCaits2RuS8buCt","https://ap.wps.com/l/cbCaits2RuS8buCt","pdf",1334662,1,50,"English","en",105,"# Acknowledgments\n# Abstract\n# List of Figures\n# List of Tables\n# Chapter 1 - Introduction\n## Background\n## Problem Statement\n## Research Aims and Objectives\n### Research Questions\n## Limitations of the study\n## Structure of the Thesis\n# Chapter 2 - Literature Review\n## Introduction\n## Fake News Detection\n## Techniques Used for Fake News Detection","[{\"question\":\"What is the main goal of the thesis?\",\"answer\":\"To create and assess a machine learning algorithm that uses NLP to identify and classify fake or misleading news for reliable information verification.\"},{\"question\":\"Which models and techniques are used for fake news detection?\",\"answer\":\"The study trains multiple machine learning and deep learning models, including LSTM, along with preprocessing and feature extraction methods such as tokenization, stop-word removal, and lemmatization.\"},{\"question\":\"How is model performance evaluated in the research?\",\"answer\":\"Performance is assessed using accuracy, precision, recall, and F1-score, and the results compare LSTM against traditional methods as well as evaluate hybrid model strategies.\"}]","Fake News Detection - 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