基于SVD的Facebook假新闻检测BERT-LSTM

S. T. S., P. Sreeja
{"title":"基于SVD的Facebook假新闻检测BERT-LSTM","authors":"S. T. S., P. Sreeja","doi":"10.1109/CSI54720.2022.9923970","DOIUrl":null,"url":null,"abstract":"Millions of individuals throughout the world consider several social networking. The Network sites/apps, such as WhatsApp, Instagram, Twitter, and Facebook, are essential information sources due to their vast user bases. Social networking platforms provide hands-on connectivity with others and ease of use to the down-market that radio and television broadcasters are unable to provide - multi-way communication. By modifying the original content or a full parody presented as fact, negative influencers can take advantage of the freedom provided by social networks and wilfully spread any misinformation. The spread of false information can happen in the blink of an eye, potentially deceiving the general public and spreading to other communities. Despite this awareness, social media platforms continue to spread dubious material. Even though many of them are still passed off as fact, hoaxes continue to be a major problem. To get around this problem, this study proposed a potent technique called BERT-LSTM to identify false information on Facebook. The accuracy of the suggested BERT-LSTM approach is 0.828.","PeriodicalId":221137,"journal":{"name":"2022 International Conference on Connected Systems & Intelligence (CSI)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"BERT-LSTM for Fake News Detection on Facebook Using SVD\",\"authors\":\"S. T. S., P. Sreeja\",\"doi\":\"10.1109/CSI54720.2022.9923970\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Millions of individuals throughout the world consider several social networking. The Network sites/apps, such as WhatsApp, Instagram, Twitter, and Facebook, are essential information sources due to their vast user bases. Social networking platforms provide hands-on connectivity with others and ease of use to the down-market that radio and television broadcasters are unable to provide - multi-way communication. By modifying the original content or a full parody presented as fact, negative influencers can take advantage of the freedom provided by social networks and wilfully spread any misinformation. The spread of false information can happen in the blink of an eye, potentially deceiving the general public and spreading to other communities. Despite this awareness, social media platforms continue to spread dubious material. Even though many of them are still passed off as fact, hoaxes continue to be a major problem. To get around this problem, this study proposed a potent technique called BERT-LSTM to identify false information on Facebook. The accuracy of the suggested BERT-LSTM approach is 0.828.\",\"PeriodicalId\":221137,\"journal\":{\"name\":\"2022 International Conference on Connected Systems & Intelligence (CSI)\",\"volume\":\"72 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-08-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 International Conference on Connected Systems & Intelligence (CSI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CSI54720.2022.9923970\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Conference on Connected Systems & Intelligence (CSI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CSI54720.2022.9923970","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

全世界数以百万计的人都在使用各种社交网络。网络网站/应用程序,如WhatsApp、Instagram、Twitter和Facebook,由于其庞大的用户基础,是必不可少的信息来源。社交网络平台为低端市场提供了与他人的直接联系和易用性,这是广播和电视广播公司无法提供的——多路通信。负面影响者可以通过修改原始内容或将其完全恶搞为事实,利用社交网络提供的自由,肆意传播任何错误信息。虚假信息的传播可能发生在眨眼之间,有可能欺骗普通公众并传播到其他社区。尽管有这种意识,社交媒体平台仍在继续传播可疑材料。尽管其中许多仍然被当作事实,但恶作剧仍然是一个主要问题。为了解决这个问题,这项研究提出了一种名为BERT-LSTM的有效技术来识别Facebook上的虚假信息。BERT-LSTM方法的准确率为0.828。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
BERT-LSTM for Fake News Detection on Facebook Using SVD
Millions of individuals throughout the world consider several social networking. The Network sites/apps, such as WhatsApp, Instagram, Twitter, and Facebook, are essential information sources due to their vast user bases. Social networking platforms provide hands-on connectivity with others and ease of use to the down-market that radio and television broadcasters are unable to provide - multi-way communication. By modifying the original content or a full parody presented as fact, negative influencers can take advantage of the freedom provided by social networks and wilfully spread any misinformation. The spread of false information can happen in the blink of an eye, potentially deceiving the general public and spreading to other communities. Despite this awareness, social media platforms continue to spread dubious material. Even though many of them are still passed off as fact, hoaxes continue to be a major problem. To get around this problem, this study proposed a potent technique called BERT-LSTM to identify false information on Facebook. The accuracy of the suggested BERT-LSTM approach is 0.828.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
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