{"title":"Machine Learning Algorithms For COVID-19 Fake News Detection","authors":"Nessrine Raggad, Nouha Arfaoui","doi":"10.1109/SETIT54465.2022.9875563","DOIUrl":null,"url":null,"abstract":"Fake news corresponds to distributed information which is not true. It becomes popularized during the 2016 U.S. elections. With the spread of COVID-19 and becoming an epidemic, much information is exchanged around the world. A part of this information is fake having a negative impact on mental health and psychological well-being of people. Because of the importance of this issue, we propose in this work applying several machine learning algorithms to detect COVID-19 fake news. We propose, also, several metrics to evaluate those models and to choose the best among them. Compared to the existing works, we use four classes: Fake, Mostly Fake, True and Mostly True.","PeriodicalId":126155,"journal":{"name":"2022 IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SETIT54465.2022.9875563","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Fake news corresponds to distributed information which is not true. It becomes popularized during the 2016 U.S. elections. With the spread of COVID-19 and becoming an epidemic, much information is exchanged around the world. A part of this information is fake having a negative impact on mental health and psychological well-being of people. Because of the importance of this issue, we propose in this work applying several machine learning algorithms to detect COVID-19 fake news. We propose, also, several metrics to evaluate those models and to choose the best among them. Compared to the existing works, we use four classes: Fake, Mostly Fake, True and Mostly True.
假新闻对应的是不真实的散布信息。它在2016年美国大选期间变得流行起来。随着COVID-19的传播并成为流行病，世界各地交换了大量信息。这些信息中的一部分是虚假的，对人们的精神健康和心理健康产生了负面影响。由于这个问题的重要性，我们在这项工作中建议应用几种机器学习算法来检测COVID-19假新闻。我们还提出了几个指标来评估这些模型并从中选择最好的模型。与现有作品相比，我们使用了四个类:Fake, most Fake, True和most True。
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