[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126885-en":3,"doc-seo-126885-105":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},126885,2336474466712,"Maeve","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Leveraging Machine Learning for Browser-Based Detection of Misinformation - Towards User-Empowered News Consumption","The surge of fake news on digital platforms undermines trust, decision-making, and information integrity, while rapidly evolving tactics strain existing defenses. This study proposes a user-friendly browser plugin that applies machine learning for real-time fake news detection, enabling individuals to actively counter misinformation. It reviews and evaluates prior techniques, comparing algorithms through careful data preparation and model refinement, with focus on textual features and class balancing. Ethical, legal, and social safeguards guide responsible deployment, bias mitigation, and copyright adherence.","s afina show kat , ar a an d Olu w a s eun , Buk ky Afolabi (2 0 2 4) Lever a ging M achin e Learnin g for Brow s er-Bas ed Detection of Misinform ation: Towar d s U s er-Empow er e d N ew s Con sumption . In: 2 0 2 3 2 8 th Intern ation al Confer enc e on Autom ation an d Computing (ICAC) . IEEE . ISBN 9 7 9-8-3 5 0 3-3 5 8 5-9  \nDownloa d e d from : [http :// sur e . sun d erl an d . ac . uk/id/ e print / 1 8 3 3 1 /](http :// sur e . sun d erl an d . ac . uk/id/ e print / 1 8 3 3 1 /)  \nU s a g e g uid eli n e s  \nPle a s e r efer to th e u s a g e guid eline s at [http :// sur e . sun d erl an d . ac. uk/ policies. html](http :// sur e . sun d erl an d . ac. uk/ policies. html) or altern atively cont act [sur e @ sun d erlan d. ac . uk](sur e @ sun d erlan d. ac . uk).  \nLeveraging Machine Learning for Browser-Based Detection of Misinformation: Towards User-Empowered News Consumption*  \nOluwaseun Bukky Afolabi 1 , Safina Showkat Ara2  \nFaculty of Technology, Department of Computer Science  \nUniversity of Sunderland, United Kingdom  \n[afolabi.rob@gmail.com](afolabi.rob@gmail.com), [safina.ara@sunderland.ac.uk](safina.ara@sunderland.ac.uk)  \nAbstract—The surge of fake news on digital platforms presentsa pressing societal concern, undermining trust and decisionmaking processes. The reliability of information, crucial for individuals and societies, faces unprecedented challenges. The rapid evolution of fake news tactics exacerbates this problem, demanding constant adaptation of countermeasures. In response, this study proposes an innovative solution: a userfriendly browser plugin employing machine learning for realtime fake news detection. We conduct a thorough examination of existing techniques, evaluating various algorithms to enhance accuracy. Through rigorous data preparation and algorithm refinement, we achieve significant improvements, emphasizing the importance of textual features and class balancing. The research extends beyond theory with the development and deployment of a practical browser plugin, enabling users to actively combat misinformation. Ethical, legal, and social considerations are integral, ensuring responsible deployment, bias mitigation, and adherence to copyright. The study advocates for ongoing refinement, highlighting the persistent relevance of fake news detection in an information-driven society.  \nIndex Terms—fakenews, machine learning, browser plugin, Misinformation, Infodemic  \nI. INTRODUCTION  \nIn the digital age, the widespread dissemination of misinformation and fake news presents a significant threat to information integrity, public discourse, and societal stability. Addressing this urgent challenge requires robust mechanisms for detecting and combating fake news effectively. This paper aims to contribute to these efforts through a comprehensive investigation into fake news detection, leveraging advanced machine learning techniques and insights from natural language processing research.  \nOur primary objective is to develop and evaluate machine learning models capable of accurately identifying fake news articles among the vast online content. By doing so, we aim to enhance the authenticity and reliability of information accessed by individuals worldwide. Our research provides insights into the nature and scope of the fake news problem, reviews pertinent literature, and proposes a methodological approach to address this pervasive issue.  \nWe employ a diverse range of machine learning algorithms, including traditional classifiers and cutting-edge transformer-  \nbased models, to explore the efficacy of different methodologies in fake news detection. Through systematic testing of techniques for data preprocessing, feature engineering, and model optimization, we aim to uncover effective strategies for distinguishing between genuine and fabricated news articles.  \nThe principal results of our experiment are expected to provide valuable insights into the performance of different machine learning mo","cbCaibSgYYJoZzyi","https://ap.wps.com/l/cbCaibSgYYJoZzyi","pdf",448188,1,"English","en",105,"# Introduction\n## Machine learning models for fake news identification\n## Algorithms and methodology scope\n## Paper structure\n# Literature Review\n## Machine Learning for Fake News Detection\n## Browser Plugins: Bringing Detection to Users\n## Limitations and Future Directions","[{\"question\":\"What problem does the study address?\",\"answer\":\"The study addresses the spread of misinformation and fake news on digital platforms and the resulting threats to information integrity, public discourse, and societal stability.\"},{\"question\":\"What solution does the study propose?\",\"answer\":\"It proposes a user-friendly browser plugin that uses machine learning to detect fake news in real time while supporting user-empowered news consumption.\"},{\"question\":\"Which aspects of model development are emphasized?\",\"answer\":\"The work emphasizes thorough data preparation, algorithm refinement, the role of textual features, and class balancing to improve detection accuracy.\"}]","Leveraging Machine Learning for Browser-Based Detection of Misinformation - 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