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False News Detection with Machine Learning","","False news comprises false, fake, or misleading information presented as real news, and its spread on the internet has increased significantly. This paper studies automatic false news detection using machine learning and natural language processing, focusing on which techniques work best. It defines what constitutes false news, compares it with other misleading information types, reviews prior research, then reports experiments on four datasets with 10 machine learning methods. Results indicate passive aggressive algorithms, support vector machines, and random forests are most effective, while richer tasks require more complex models.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/are-strong-baselines-enough-false-news-detection-with-machine-learning/290474/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/are-strong-baselines-enough-false-news-detection-with-machine-learning/290474.png","ImageObject",300,407,{"name":92,"@type":93},"Violet","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",13,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What does the paper mean by “false news” and why is it important to detect?","Question",{"text":113,"@type":114},"False news refers to false, fake, or misleading information presented as real news. The paper argues that its growing spread threatens journalism, can cause political turmoil, and impacts daily life.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Which machine learning methods does the paper report as most efficient for false news detection?",{"text":118,"@type":114},"The experiments conclude that passive aggressive algorithms, support vector machines, and random forests are the most effective methods for automatic false news detection.",{"name":120,"@type":111,"acceptedAnswer":121},"What additional challenges does the paper highlight for more complex false-news scenarios?",{"text":122,"@type":114},"It concludes that tasks such as multiple levels of identifying false news or detecting computer-generated false news require more complex machine learning models.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290474,1789641769,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145,"preview_page_count":4},4398048950312,"https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908","This is an electronic reprint of the original article. This reprint may differ from the original in pagination and typographic detail.  \nAuthor(s): Aslan, Lara; Ptaszynski, Michal; Jauhiainen, Jukka  \nTitle: Are Strong Baselines Enough? False News Detection with Machine  \nLearning  \nYear: 2024  \nVersion: Publisher’s versio  \nLicense: CC BY  \nPlease cite the original version:  \nAslan, L. , Ptaszynski, M. & Jauhiainen, J. (2024) . Are Strong Baselines Enough? False News Detection with Machine Learning. Future Internet, 16(9), Article 322.  \n[https://doi.org/10.3390/fi16090322](https://doi.org/10.3390/fi16090322)  \nfuture internet  \nArticle  \nAre Strong Baselines Enough? False News Detection with Machine Learning  \nLara Aslan 1,†, Michal Ptaszynski 1, *,† and Jukka Jauhiainen 2, *  \nCitation: Aslan, L.; Ptaszynski, M.; Jauhiainen, J. Are Strong Baselines Enough? False News Detection with Machine Learning. Future Internet 2024, 16, 322. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/fi16090322](10.3390/fi16090322)  \nAcademic Editor: Filipe Portela  \nReceived: 13 August 2024  \nRevised: 26 August 2024  \nAccepted: 27 August 2024  \nPublished: 5 September 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Text Information Processing Laboratory, Kitami Institute of Technology, Kitami 090-8507, Japan; [m3245380016@std.kitami-it.ac.jp](m3245380016@std.kitami-it.ac.jp)  \n2 School of Information Technology, Oulu University of Applied Sciences, 90570 Oulu, Finland  \n* [Correspondence: michal@mail.kitami-it.ac.jp](Correspondence: michal@mail.kitami-it.ac.jp) (M.P.); [jukka.jauhiainen@oamk.fi](jukka.jauhiainen@oamk.fi) (J.J.)† These authors contributed equally to this work.  \nAbstract: False news refers to false, fake, or misleading information presented as real news. In recent years, there has been a noticeable increase in false news on the Internet. The goal of this paper was to study the automatic detection of such false news using machine learning and natural language processing techniques and to determine which techniques work the most effectively. This article first studies what constitutes false news and how it differs from other types of misleading information. We also study the results achieved by other researchers on the same topic. After building a foundation to understand false news and the various ways of automatically detecting it, this article provides its own experiments. These experiments were carried out on four different datasets, one that was made just for this article, using 10 different machine learning methods. The results of this article were satisfactory and provided answers to the original research questions set up at the beginning of this article. This article could determine from the experiments that passive aggressive algorithms, support vector machines, and random forests are the most efficient methods for automatic false news detection. This article also concluded that more complex experiments, such as using multiple levels of identifying false news or detecting computer-generated false news, require more complex machine learning models.  \nKeywords: machine learning; natural language processing; false news detection; artificial intelligence; ChatGPT  \n1. Introduction  \nFalse news can be described as false or misleading information created to be widely shared for different purposes, such as to generate revenue, promote, or affect the opinions of a target group before a big event. Recent years have seen the spread of false news, especially on the Internet [1–3] . This issue needs to be addressed, as the growing spread of false news threatens journalism, can cause political turmoil, an","cbCaisvjl2Z4Xoil","https://ap.wps.com/l/cbCaisvjl2Z4Xoil","pdf",6207982,33,"English","# Introduction\n## False news and the need for automation\n## Research goals and approach\n# Abstract-like Summary of Methods and Findings","[{\"question\":\"What does the paper mean by “false news” and why is it important to detect?\",\"answer\":\"False news refers to false, fake, or misleading information presented as real news. The paper argues that its growing spread threatens journalism, can cause political turmoil, and impacts daily life.\"},{\"question\":\"Which machine learning methods does the paper report as most efficient for false news detection?\",\"answer\":\"The experiments conclude that passive aggressive algorithms, support vector machines, and random forests are the most effective methods for automatic false news detection.\"},{\"question\":\"What additional challenges does the paper highlight for more complex false-news scenarios?\",\"answer\":\"It concludes that tasks such as multiple levels of identifying false news or detecting computer-generated false news require more complex machine learning models.\"}]","Are Strong Baselines Enough? False News Detection with Machine Learning | PDF",83]