[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126028-en":3,"doc-seo-126028-105":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":11,"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},126028,2336474466412,"Ezra","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Development of a Machine Learning Algorithm for Fake News Detection","Extensive technological growth and the rapid spread of social media have amplified the creation and dissemination of fake news, leading to public mistrust, fear, harm, and misinformation at individual, organizational, and societal levels. To mitigate these impacts, a supervised machine learning approach was developed to classify Twitter data as fake news. The pipeline covers data acquisition, preprocessing, transformation, model training with Naïve Bayes, decision tree, and SVM, and evaluation using accuracy, precision, recall, and F1-score. Results show decision tree achieves the highest performance, reaching 100% accuracy for text and strong F1 for metadata-based classification.","Development of a machine learning algorithm for fake news detection  \nNur Atiqah Sia Abdullah1,3, Nur Ida Aniza Rusli2,3, Nurshaheeda Shazlin Yuslee1  \n1College of Computing, Informatics and Mathematics, Universiti Teknologi MARA, Shah Alam, Malaysia 2College of Computing, Informatics and Mathematics, Universiti Teknologi MARA, Cawangan Negeri Sembilan, Kampus Kuala Pilah,  \nNegeri Sembilan, Malaysia  \n3Knowledge and Software Engineering Research Group (KASERG), Universiti Teknologi MARA, Shah Alam, Malaysia  \n\n| Article history:\u003Cbr>Received Nov 3, 2023 Revised Apr 25, 2024 Accepted May 7, 2024 | With the extensive technological advancements and expansion, the persistent issues regarding the creation and rapid dissemination of fake news have become a prevalent and recurrent concern. The manipulation of news content has critical repercussions, such as causing public mistrust, fear, harm, and misinformation. Addressing that, this study developed a supervised machine learning algorithm that can accurately classify social media data as fake news. The methodology of the proposed fake news detection model involved five main components: data acquisition from Twitter, data preprocessing, data transformation, model development using Naïve Bayes, decision tree, and support vector machine (SVM) and model evaluation using accuracy, precision, recall and F1-score. The results revealed that decision tree recorded the highest accuracy for both textual data (100%) and metadata (94.54%) and consistently outperformed both Naïve Bayes and SVM in terms of precision, recall, and F1-score metrics, with a score of 100% for the classification of textual data-based datasets. Regarding the metadata-based classification, decision tree also demonstrated excellent performance, with the highest F1-score of 94% for fake news data. Meanwhile, SVM exhibited the highest precision and recall performance for the metadata-based classification. Overall, the application of the decision tree classifier was deemed the most effective in Twitter fake news detection.\u003Cbr>This is an open access article under the CC BY-SA license.\u003Cbr> |\n| --- | --- |\n| Keywords:\u003Cbr>Decision tree\u003Cbr>Fake news\u003Cbr>Machine learning algorithm Naïve Bayes\u003Cbr>Social media\u003Cbr>Support vector machine Twitter |  |\n\nCorresponding Author:  \nNur Ida Aniza Rusli  \nCollege of Computing, Informatics and Mathematics, Universiti Teknologi MARACawangan Negeri Sembilan, Kampus Kuala Pilah  \n72000 Kuala Pilah, Negeri Sembilan, Malaysia Email: [idaaniza@uitm.edu.my](idaaniza@uitm.edu.my)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nTechnological advancements and expansion have contributed to the development and improvement of communication means. The growing dominance of technologies and the proliferation of social media platforms like Twitter and Facebook have transformed how we communicate with one another. Despite the significant benefits of such advancements, there are negative repercussions to the members of society [1], [2] . The persistent issues of fake news distorting information are not part of a new phenomenon. When it comes to the dissemination of fake news and false information, particularly across social media platforms, there are critical implications at the individual, organizational, and societal levels.  \nThe circulation of fake news content can create public bias [3], confusion [3], [4], panic [5], and mistrust [6], as well as poor confidence in the government or other relevant institutions [7] . Considering these  \ncritical repercussions, addressing issues concerning fake news on social media platforms is pivotal. Fake news and spam messages share similar attributes. For example, there are common issues like grammatical errors [8], deliberate manipulation of public opinions [9], and recurrent inclination towards dissemination of inaccurate or misleading information [10] . Additionally, fake news uses of the same limited lexicon for content simplification [11] .  \nMachine learning have been wid","cbCaidjwJrELaQXR","https://ap.wps.com/l/cbCaidjwJrELaQXR","pdf",475053,1,12,"English","en",105,"# Abstract\n# Introduction\n## Impact of fake news on society\n## Challenges in fake news detection\n# Related Work\n## Naïve Bayes early approach\n## Feature-based machine learning methods\n# Proposed Methodology\n## Data acquisition and preprocessing\n## Model development and evaluation","[{\"question\":\"What problem does the study address in social media communication?\",\"answer\":\"The study targets the persistent creation and rapid dissemination of fake news, which can distort information and cause public mistrust, fear, harm, and misinformation.\"},{\"question\":\"How is the proposed fake news detection model constructed and evaluated?\",\"answer\":\"The methodology includes data acquisition from Twitter, data preprocessing, data transformation, training Naïve Bayes, decision tree, and SVM, and evaluation using accuracy, precision, recall, and F1-score.\"},{\"question\":\"Which classifier performed best for Twitter fake news detection and on what data type?\",\"answer\":\"The decision tree classifier achieved the highest accuracy for textual data (100%) and demonstrated strong overall performance for metadata-based classification, including the highest F1-score for fake news data.\"}]","Development of a Machine Learning Algorithm for Fake News Detection | 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problem does the study address in social media communication?","Question",{"text":76,"@type":77},"The study targets the persistent creation and rapid dissemination of fake news, which can distort information and cause public mistrust, fear, harm, and misinformation.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How is the proposed fake news detection model constructed and evaluated?",{"text":81,"@type":77},"The methodology includes data acquisition from Twitter, data preprocessing, data transformation, training Naïve Bayes, decision tree, and SVM, and evaluation using accuracy, precision, recall, and F1-score.",{"name":83,"@type":74,"acceptedAnswer":84},"Which classifier performed best for Twitter fake news detection and on what data type?",{"text":85,"@type":77},"The decision tree classifier achieved the highest accuracy for textual data (100%) and demonstrated strong overall performance for metadata-based classification, including the highest F1-score for fake news 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