Vemula Anil Kumar, Yeruva Sai Prakash Reddy, Vudathu Teja Sai Balaram, Gutta Tei Bhargav, Vinod Kumar
{"title":"Identifying Fake News in Real Time","authors":"Vemula Anil Kumar, Yeruva Sai Prakash Reddy, Vudathu Teja Sai Balaram, Gutta Tei Bhargav, Vinod Kumar","doi":"10.1109/ICICT57646.2023.10134383","DOIUrl":null,"url":null,"abstract":"Today's society faces a significant challenge in the form of fake news. It is essential to be able to spot instances of false news as they occur in real time in order to stop their spread and lessen the damage they do. This research study describes a thorough method for spotting false news in real time using machine learning techniques. The method is presented in the context of this research article. This study offers a system that differentiates between authentic and false news by combining a variety of characteristics and classifiers in a unified fashion. The linguistic and contextual elements, as well as data on user activity, are utilized by proposed technique in the identification of bogus news. Proposed system is tested on a real-time dataset and find that it has a high rate of accuracy as well as precision when recognizing false news.","PeriodicalId":126489,"journal":{"name":"2023 International Conference on Inventive Computation Technologies (ICICT)","volume":"104 2","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 International Conference on Inventive Computation Technologies (ICICT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICICT57646.2023.10134383","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
Today's society faces a significant challenge in the form of fake news. It is essential to be able to spot instances of false news as they occur in real time in order to stop their spread and lessen the damage they do. This research study describes a thorough method for spotting false news in real time using machine learning techniques. The method is presented in the context of this research article. This study offers a system that differentiates between authentic and false news by combining a variety of characteristics and classifiers in a unified fashion. The linguistic and contextual elements, as well as data on user activity, are utilized by proposed technique in the identification of bogus news. Proposed system is tested on a real-time dataset and find that it has a high rate of accuracy as well as precision when recognizing false news.
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