{"title":"利用多通道深度神经网络检测假新闻","authors":"Meenakshi A. Thalor, Mayuri Garad","doi":"10.59890/ijist.v1i5.684","DOIUrl":null,"url":null,"abstract":"Fake news has become a pervasive issue in today's digital age, posing significant challenges to information integrity and trustworthiness. In this study, we propose a novel approach for the detection of fake news using MultiChannel Deep Neural Networks (MC-DNNs). Our research aims to address the limitations of traditional fake news detection methods by leveraging the power of deep learning and multiple data sources.","PeriodicalId":503863,"journal":{"name":"International Journal of Integrated Science and Technology","volume":"2 2","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Fake News Detection Using MultiChannel Deep Neural Networks\",\"authors\":\"Meenakshi A. Thalor, Mayuri Garad\",\"doi\":\"10.59890/ijist.v1i5.684\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Fake news has become a pervasive issue in today's digital age, posing significant challenges to information integrity and trustworthiness. In this study, we propose a novel approach for the detection of fake news using MultiChannel Deep Neural Networks (MC-DNNs). Our research aims to address the limitations of traditional fake news detection methods by leveraging the power of deep learning and multiple data sources.\",\"PeriodicalId\":503863,\"journal\":{\"name\":\"International Journal of Integrated Science and Technology\",\"volume\":\"2 2\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-11-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Integrated Science and Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.59890/ijist.v1i5.684\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Integrated Science and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.59890/ijist.v1i5.684","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Fake News Detection Using MultiChannel Deep Neural Networks
Fake news has become a pervasive issue in today's digital age, posing significant challenges to information integrity and trustworthiness. In this study, we propose a novel approach for the detection of fake news using MultiChannel Deep Neural Networks (MC-DNNs). Our research aims to address the limitations of traditional fake news detection methods by leveraging the power of deep learning and multiple data sources.
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