[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125564-en":3,"doc-seo-125564-105":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":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},125564,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Fake news detection - a systematic literature review of machine learning algorithms and datasets","Fake news (false news created for wide dissemination with malicious intent) drives significant political, economic, and social impacts through digital and social media channels. This exploratory qualitative study analyzes the machine learning algorithms and datasets used for training in the literature, applying a research protocol to identify relevant studies. Reported models include Stacking Method, BiRNN, and CNN with accuracies near 99.9%, 99.8%, and 99.8%. However, most datasets are controlled (e.g., Kaggle) or not updated in real time from social networks, leaving fewer studies for social-platform settings.","Journal on Interactive Systems, 2023, 14:1, doi: 10.5753/jis.2023.3020  \n This work is licensed under a Creative Commons Attribution 4.0 International License.  \nFake news detection: a systematic literature review of machine learning algorithms and datasets  \nHumberto Fernandes Villela  [ [Universidade FUMEC | humberto.villela@gmail.com](Universidade FUMEC | humberto.villela@gmail.com) ]  \nFábio Corrêa  [ [Universidade FUMEC | fabiocontact@gmail.com](Universidade FUMEC | fabiocontact@gmail.com) ]  \nJurema Suely de Araújo Nery Ribeiro  [ [Universidade FUMEC | jurema.nery@gmail.com](Universidade FUMEC | jurema.nery@gmail.com) ]  \nAir Rabelo  [ [Universidade FUMEC | air@fumec.br](Universidade FUMEC | air@fumec.br) ]  \nDárlinton Barbosa Feres Carvalho  [ Universidade Federal de São Joã[o del-Rei | darlinton@acm.org](o del-Rei | darlinton@acm.org) ]  \nAbstract  \nFake news (i.e., false news created to have a high capacity for dissemination and malicious intentions) is a problem of great interest to society today since it has achieved unprecedented political, economic, and social impacts. Taking advantage of modern digital communication and information technologies, they are widely propagated through social media, being their use intentional and challenging to identify. In order to mitigate the damage caused by fake news, researchers have been seeking the development of automated mechanisms to detect them, such as algorithms based on machine learning as well as the datasets employed in this development. This research aims to analyze the machine learning algorithms and datasets used in training to identify fake news published in the literature. It is exploratory research with a qualitative approach, which uses a research protocol to identify studies with the intention of analyzing them. As a result, we have the algorithms Stacking Method, Bidirectional Recurrent Neural Network (BiRNN), and Convolutional Neural Network (CNN), with 99.9%, 99.8%, and 99.8% accuracy, respectively. Although this accuracy is expressive, most of the research employed datasets in controlled environments (e.g., Kaggle) or without information updated in real-time (from social networks) . Still, only a few studies have been applied in social network environments, where the most significant dissemination of disinformation occurs nowadays. Kaggle was the platform identified with the most frequently used datasets, being succeeded by Weibo, FNC-1, COVID-19 Fake News, and Twitter. For future research, studies should be carried out in addition to news about politics, the area that was the primary motivator for the growth of research from 2017, and the use of hybrid methods for identifying fake news.  \nKeywords: Algorithms, datasets, accuracy, fake news, artificial intelligence.  \n1 Introduction  \nCurrently, the term fake news is on the rise as this type of news can remarkably influence society, promoting significant political, economic, or social impacts (Zhang et al., 2016; Islam et al., 2020) . They are false news created to be highly broadcastable, usually with malicious intent (i.e., to deceive, cause ambiguity, or falsehood) .  \nIn the political context, specifically, Almeida et al. (2021) point out the impacts of fake news on the US presidential elections in 2016, having President Donald Trump elected. In Brazil, similar impacts were imputed to the election of President Jair Bolsonaro in 2018. Due to the behavior of many Brazilian voters relying on social media as the primary source to access news, this channel is fruitful for the proliferation of fake news (ALMEIDA et al., 2021) .  \nDue to the relevant impact this type of news has caused on society, researchers have been seeking to develop ways to detect them. Using algorithms for the automated identification of fake news presents itself as a promising line of research. The quality of these algorithms is commonly verified by accuracy, which is the measure of correctness in classifying whether a news item is tru","cbCaiix8hgS9sODj","https://ap.wps.com/l/cbCaiix8hgS9sODj","pdf",448671,1,12,"English","en",105,"# Abstract\n# 1 Introduction\n## Background and societal impact\n## Automated detection and accuracy\n## Relationship between algorithms, datasets, and language\n## Related work and reported performance","[{\"question\":\"What problem does the study address?\",\"answer\":\"The study addresses fake news: false information designed to be highly spreadable and harmful, often amplified through digital and social media channels.\"},{\"question\":\"Which machine learning algorithms are reported as effective in the review?\",\"answer\":\"The review reports strong results for Stacking Method, Bidirectional Recurrent Neural Network (BiRNN), and Convolutional Neural Network (CNN), with accuracies around 99.9%, 99.8%, and 99.8% respectively.\"},{\"question\":\"Why does the paper emphasize datasets used in training?\",\"answer\":\"Algorithm quality is linked to the specific task, language, and news style, which depend heavily on the datasets used for training. The authors recommend broader dataset use, including languages other than English.\"}]","Fake news detection - a systematic literature review of machine learning algorithms and datasets | PDF",1785899884,30,{"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},"fake-news-detection-a-systematic-literature-review-of-machine-learning-algorithms-and-datasets","",{"@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/fake-news-detection-a-systematic-literature-review-of-machine-learning-algorithms-and-datasets/125564/",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","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the study address?","Question",{"text":75,"@type":76},"The study addresses fake news: false information designed to be highly spreadable and harmful, often amplified through digital and social media channels.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning algorithms are reported as effective in the review?",{"text":80,"@type":76},"The review reports strong results for Stacking Method, Bidirectional Recurrent Neural Network (BiRNN), and Convolutional Neural Network (CNN), with accuracies around 99.9%, 99.8%, and 99.8% respectively.",{"name":82,"@type":73,"acceptedAnswer":83},"Why does the paper emphasize datasets used in training?",{"text":84,"@type":76},"Algorithm quality is linked to the specific task, language, and news style, which depend heavily on the datasets used for training. The authors recommend broader dataset use, including languages other than English.","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},[93,97,101,105,110,115,120,122,127,130,134],{"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":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]