Fake News Detection using Naive Bayes Classifier and Passive Aggressive Classifier

Valdet Shabani, Abdullah Havolli, A. Maraj, Lorik Fetahu
{"title":"Fake News Detection using Naive Bayes Classifier and Passive Aggressive Classifier","authors":"Valdet Shabani, Abdullah Havolli, A. Maraj, Lorik Fetahu","doi":"10.1109/MECO58584.2023.10155036","DOIUrl":null,"url":null,"abstract":"The rapid growth of fake news, as well as its damaging effects on every area of our lives, has increased the demand for detecting and combating fake news. As a result, distinguishing between real and fake news is critical. However, due to the massive amount of information generated every minute on the Internet, making this distinction manually is extremely difficult. This study will suggest an approach for detecting fake news and a mechanism for implementing it on social media. In this paper, the Naive Bayes Classifier and Passive Aggressive Classifier techniques will be used to detect fake news. The results will prove that the problem of identifying fake news is possible if Machine learning and Natural Language Processing algorithm are used.","PeriodicalId":187825,"journal":{"name":"2023 12th Mediterranean Conference on Embedded Computing (MECO)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 12th Mediterranean Conference on Embedded Computing (MECO)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MECO58584.2023.10155036","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

The rapid growth of fake news, as well as its damaging effects on every area of our lives, has increased the demand for detecting and combating fake news. As a result, distinguishing between real and fake news is critical. However, due to the massive amount of information generated every minute on the Internet, making this distinction manually is extremely difficult. This study will suggest an approach for detecting fake news and a mechanism for implementing it on social media. In this paper, the Naive Bayes Classifier and Passive Aggressive Classifier techniques will be used to detect fake news. The results will prove that the problem of identifying fake news is possible if Machine learning and Natural Language Processing algorithm are used.
基于朴素贝叶斯分类器和被动攻击分类器的假新闻检测
假新闻的快速增长,以及它对我们生活各个领域的破坏性影响,增加了发现和打击假新闻的需求。因此,区分真假新闻至关重要。然而,由于互联网上每分钟都会产生大量的信息,手动进行这种区分是非常困难的。本研究将提出一种检测假新闻的方法以及在社交媒体上实施假新闻的机制。在本文中,将使用朴素贝叶斯分类器和被动攻击分类器技术来检测假新闻。结果将证明,如果使用机器学习和自然语言处理算法,识别假新闻的问题是可能的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信