A smart System for Fake News Detection Using Machine Learning

2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT) Pub Date : 2019-09-01 DOI:10.1109/ICICT46931.2019.8977659

Anjali Jain, Avinash Shakya, Harsh Khatter, A. Gupta

{"title":"A smart System for Fake News Detection Using Machine Learning","authors":"Anjali Jain, Avinash Shakya, Harsh Khatter, A. Gupta","doi":"10.1109/ICICT46931.2019.8977659","DOIUrl":null,"url":null,"abstract":"Most of the smart phone users prefer to read the news via social media over internet. The news websites are publishing the news and provide the source of authentication. The question is how to authenticate the news and articles which are circulated among social media like WhatsApp groups, Facebook Pages, Twitter and other micro blogs & social networking sites. It is harmful for the society to believe on the rumors and pretend to be a news. The need of an hour is to stop the rumors especially in the developing countries like India, and focus on the correct, authenticated news articles. This paper demonstrates a model and the methodology for fake news detection. With the help of Machine learning and natural language processing, author tried to aggregate the news and later determine whether the news is real or fake using Support Vector Machine. The results of the proposed model is compared with existing models. The proposed model is working well and defining the correctness of results upto 93.6% of accuracy.","PeriodicalId":412668,"journal":{"name":"2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"78","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICICT46931.2019.8977659","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 78

Abstract

Most of the smart phone users prefer to read the news via social media over internet. The news websites are publishing the news and provide the source of authentication. The question is how to authenticate the news and articles which are circulated among social media like WhatsApp groups, Facebook Pages, Twitter and other micro blogs & social networking sites. It is harmful for the society to believe on the rumors and pretend to be a news. The need of an hour is to stop the rumors especially in the developing countries like India, and focus on the correct, authenticated news articles. This paper demonstrates a model and the methodology for fake news detection. With the help of Machine learning and natural language processing, author tried to aggregate the news and later determine whether the news is real or fake using Support Vector Machine. The results of the proposed model is compared with existing models. The proposed model is working well and defining the correctness of results upto 93.6% of accuracy.

查看原文本刊更多论文

使用机器学习的假新闻检测智能系统

大多数智能手机用户更喜欢通过互联网上的社交媒体阅读新闻。新闻网站既发布新闻，又提供新闻的认证来源。问题是如何验证在社交媒体上传播的新闻和文章，如WhatsApp群，Facebook页面，Twitter和其他微博和社交网站。相信谣言，假装是新闻，对社会是有害的。需要一个小时的时间来停止谣言，特别是在印度这样的发展中国家，把注意力集中在正确的、经过认证的新闻文章上。本文展示了假新闻检测的模型和方法。在机器学习和自然语言处理的帮助下，作者尝试对新闻进行聚合，然后使用支持向量机来判断新闻的真假。将所提模型的计算结果与已有模型进行了比较。该模型运行良好，结果的正确率达到93.6%。

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

2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT)

自引率

0.00%

发文量