[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117972-en":3,"doc-seo-117972-105":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},117972,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Ensemble Machine Learning Approaches for Fake News Classification","Ensemble machine learning is leveraged to classify fake news in an environment where misinformation and disinformation spread rapidly through digital platforms. The study addresses threats to public trust, credible news ecosystems, and informed democratic decision-making by building a robust ensemble model. Three state-of-the-art algorithms—LightGBM, XGBoost, and Balanced Random Forest—are trained on a diverse dataset and enhanced through token-importance emphasis. Experiments show LightGBM achieves an F1-score of 97.74% and accuracy of 97.64%, with all models outperforming previously reported baselines.","UDC 070.16-048.445:004.85 doi: 10.32620/reks.2023.4.01  \nHalyna PADALKO1, 2, 3, Vasyl CHOMKO2,  \nSergiy YAKOVLEV4, Dmytro CHUMACHENKO1  \n1 National Aerospace University “Kharkiv Aviation Institute”, Kharkiv, Ukraine  \n2 University of Waterloo, Waterloo, Canada  \n3 Balsillie School of International Affairs, Waterloo, Canada  \n4 Lodz University of Technology, Lodz, Poland  \nENSEMBLE MACHINE LEARNING APPROACHES  \nFOR FAKE NEWS CLASSIFICATION  \nIn today’s interconnected digital landscape, the proliferation offake news has become a significant challenge, with far-reaching implications for individuals, institutions, and societies. The rapid spread of misleading information undermines the credibility of genuine news outlets and threatens informed decision-making, public trust, and democratic processes. Recognizing the profound relevance and urgency of addressing this issue, this research embarked on a mission to harness the power of machine learning to combat fake news menace. This study develops an ensemble machine learning model for fake news classification. The research is targeted at spreading fake news. The research subjects are machine learning methods for misinformation classification. Methods: we employed three state-of-the-art algorithms: LightGBM, XGBoost, and Balanced Random Forest (BRF). Each model was meticulously trained on a comprehensive dataset curated to encompass a diverse range of news articles, ensuring a broad representation of linguistic patterns and styles. A distinctive feature of the proposed approach is the emphasis on token importance. By leveraging specific tokens that exhibited a high degree of influence on classification outcomes, we enhanced the precision and reliability of the developed models. The empirical results were both promising and illuminating. The LightGBM model emerged as the topperformer among the three, registering an impressive F1-score of 97. 74% and an accuracy rate of 97. 64%. Notably, all three of the proposed models consistently outperformed several existing models previously documented in academic literature. This comparative analysis underscores the efficacy and superiority of the proposed ensemble approach. In conclusion, this study contributes a robust, innovative, and scalable solution to the pressing challenge of fake news detection. By harnessing the capabilities of advanced machine learning techniques, the research findings pave the way for enhancing the integrity and veracity of information in an increasingly digitalized world, thereby safeguarding public trust and promoting informed discourse.  \nKeywords: fake news; classification; misinformation; disinformation; balanced random forest; XGBoost; LightGBM; WELFake; machine learning.  \nIntroduction  \nIn the contemporary digital age, the proliferation of misinformation and disinformation has emerged as a pressing concern. Misinformation, defined as false or inaccurate information shared without malicious intent [1], and disinformation, which is deliberately disseminated to deceive, pose significant threats to the integrity of public discourse, informed decision-making, and the fabric of democratic societies [2] . The ubiquity of digital platforms and the rapid dissemination of information have exacerbated these challenges, making it urgent for scholars, policymakers, and technologists to address them [3].  \nThe alarming spread of fake news is parallel to the challenges of misinformation and disinformation [4] . Fake news, which is often sensationalized and devoid offactual grounding, is not merely an informational concern but a societal one [5] . Its rapid dissemination can  \nsway public opinion, influence electoral outcomes, and even incite real-world harm [6]. The viral nature of fake news, propelled by social media algorithms and human cognitive biases, underscores the need for effective countermeasures to ensure the veracity of information consumed by the public [7].  \nAs the digital landscape becomes increasingly complex","cbCaia5eRqEZ8rbD","https://ap.wps.com/l/cbCaia5eRqEZ8rbD","pdf",963410,1,15,"English","en",105,"# Introduction\n## Definitions and threats of misinformation/disinformation\n## Why fake news spreads and its societal impact\n## Existing research directions\n## Need for automated fake news classification\n## Role of machine learning","[{\"question\":\"What is the main goal of the study?\",\"answer\":\"To build an ensemble machine learning model that classifies fake news effectively, improving detection and reliability.\"},{\"question\":\"Which algorithms are used in the proposed approach?\",\"answer\":\"LightGBM, XGBoost, and Balanced Random Forest (BRF) are trained and compared as ensemble-based classifiers.\"},{\"question\":\"How does the study improve classification performance?\",\"answer\":\"It emphasizes token importance by leveraging influential tokens that strongly affect classification outcomes.\"}]","Ensemble Machine Learning Approaches for Fake News Classification | PDF",1785680599,38,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"ensemble-machine-learning-approaches-for-fake-news-classification","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/ensemble-machine-learning-approaches-for-fake-news-classification/117972/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of the study?","Question",{"text":75,"@type":76},"To build an ensemble machine learning model that classifies fake news effectively, improving detection and reliability.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which algorithms are used in the proposed approach?",{"text":80,"@type":76},"LightGBM, XGBoost, and Balanced Random Forest (BRF) are trained and compared as ensemble-based classifiers.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the study improve classification performance?",{"text":84,"@type":76},"It emphasizes token importance by leveraging influential tokens that strongly affect classification outcomes.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]