[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121982-en":3,"doc-seo-121982-105":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},121982,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Fake news detection by Machine Learning in Latin America - A Systematic Review","The growing spread of fake news on social networks has become a major societal problem, and many studies use artificial intelligence, especially machine learning, to automatically detect false information. To clarify the current state of existing proposals, this work conducts a systematic literature review and organizes the body of research through a taxonomy of approaches. The focus emphasizes Latin America, where cultural aspects can introduce specific challenges for machine learning-based detection.","Proceedings of the 57th Hawaii International Conference on System Sciences | 2024  \nFake news detection by Machine Learning in Latin America: A Systematic  \nReview  \nJean Gabriel Nguema Ngomo Department of Applied Informatics UNIRIO  [mvojgnn@edu.unirio.br](mvojgnn@edu.unirio.br)  \nRaquel Torres de Paiva Department of Applied Informatics UNIRIO [raquel.paiva@edu.unirio.br](raquel.paiva@edu.unirio.br)  \nAna Cristina Bicharra Garcia Department of Applied Informatics UNIRIO [cristina.bicharra@uniriotec.br](cristina.bicharra@uniriotec.br)  \nAbstract  \nThe growing spread of fake news on social network is a major scourge in society. To combat this problem, many studies have focused on different aspects to automatically detect fake news on social networks using artificial intelligence, especially Machine Learning. In the present work, to understand the current state of existing proposals, we conducted a systematic literature review. We propose to organize this literature in the light of a taxonomy of approaches.  \nKeywords: Fake news; Misinformation;  \nDisinformation; Machine Learning; Latin America; Social Media; Social Network; Cultural aspects  \n1. Introduction  \nIn recent years, social network have consolidated themselves as the main source of information for the general population, replacing traditional reliable media such as televisions, radios, newspapers, and magazines. While they bring many benefits, such as speed and agility of communication with interconnected people, social network can also lead to serious problems such as the dissemination of fake news, rumors, hate speech, among other problems.  \nFake news can have dire consequences for society, leading individuals to make the wrong decisions. In this sense, there are several examples of these consequences. We can mention the invasion of the headquarters of the three powers of Brazil (executive, legislative, and judicial), by vandals, in January 2023, a few days after the beginning of the mandate of the elected president Ara´ujo [6]; the episode that bears resemblance to the invasion of the United States Capitol in January 2020[9] . Fake News covers any domain, distorting or inventing  \ninformation about the pandemic, vaccines, personalities, for instance. This scourge threatens a fundamental element of our society: the truth. Furthermore, all regions of the world face this kind of problem, and Latin America does not escape this sad reality, as the example above illustrates.  \nSeveral researchers have been investigating solutions to combat the spread of fake news on social network, identifying them using Artificial Intelligence, specifically Machine Learning. This type of solution would allow users of social network to discover that certain news is false before it is shared with their contacts, breaking the chain at the root.  \nDuring the present work, we found systematic reviews of such researches [27], [35], [3], [34], [4],[26], [11], [39] and [7] . However, there are few of these reviews from the perspective of Latin America, where cultural aspects may present new challenges for Machine Learning. Then in the present study, we conduct a systematic review of approaches directed to fake news detection in social network using Machine Learning with emphasis on this region, considering the last 5 years. Our main motivation is to answer the following research questions:  \n• Which approaches and Machine Learning techniques are used to detect and combat fake news in Latin America?  \n• How does Machine Learning help fighting fake news through different types of social network, specifically in Latin America?  \n• Which conceptual characteristics and limiting factors do Machine Learning applications seeking to identify fake news in social network have, specifically in Latin America?  \nThe rest of this paper is organized as follows. In section  \nURI: [https://hdl.handle.net/10125/106688](https://hdl.handle.net/10125/106688)[ ](https://hdl.handle.net/10125/106688)[978-0-9981331-7-1","cbCaidqyR89KQfEL","https://ap.wps.com/l/cbCaidqyR89KQfEL","pdf",417855,1,10,"English","en",105,"# Introduction\n## Fake news detection challenges and social media impact\n## Research motivation and questions\n# Background\n## Fake news terminology and information disorder\n## Misinformation vs. disinformation vs. malinformation\n# Methodology\n# Study results and discussion\n# Limitations and cultural challenges in Latin America\n# Conclusion","[{\"question\":\"What problem does the paper focus on regarding social networks?\",\"answer\":\"It focuses on the spread of fake news on social networks and how it can lead people to make wrong decisions and cause serious societal consequences.\"},{\"question\":\"How does the paper structure and interpret existing research?\",\"answer\":\"It performs a systematic literature review and organizes the studies using a taxonomy of machine learning approaches for fake news detection.\"},{\"question\":\"Why is Latin America emphasized in the review?\",\"answer\":\"Because cultural aspects in the region may create new challenges for machine learning applications aimed at identifying fake news.\"}]","Fake news detection by Machine Learning in Latin America - 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