Certain Investigation of Fake News Detection from Facebook and Twitter Using Artificial Intelligence Approach

被引:0
|
作者
Roy Setiawan
Vidya Sagar Ponnam
Sudhakar Sengan
Mamoona Anam
Chidambaram Subbiah
Khongdet Phasinam
Manikandan Vairaven
Selvakumar Ponnusamy
机构
[1] Universitas Airlangga,Department Management
[2] Koneru Lakshmaiah Education Foundation,Department of Computer Science and Engineering
[3] PSN College of Engineering and Technology,Department of Computer Science and Engineering
[4] International Islamic University,Department of Computer Sciences and Software Engineering, Faculty of Basic and Applied Sciences
[5] National Engineering College,Department of Information Technology
[6] Pibulsongkram Rajabhat University,School of Agricultural and Food Engineering, Faculty of Food and Agricultural Technology
[7] PSN College of Engineering and Technology,Department of Mechanical Engineering
来源
Wireless Personal Communications | 2022年 / 127卷
关键词
NLP; Hybrid SVM; Machine Learning; Fake News;
D O I
暂无
中图分类号
学科分类号
摘要
The news platform has moved from traditional newspapers to online communities in the technologically advanced area of Artificial Intelligence. Because Twitter and Facebook allow us to consume news much faster and with less restricted editing, false information continues to spread at an impressive rate and volume. Online Fake News Detection is a promising field in research and captivates the attention of researchers. The sprawl of huge chunks of misinformation in social network platforms is vulnerable to global risk. This article recommends using a Machine Learning optimization technique for automated news article classification on Facebook and Twitter. The emergence of the research is facilitated by the strategic implementation of Natural Language Processing for social forum fake news findings in order to distort news reports from non-recurrent outlets. The relent from the study is outstanding with text document frequency words, which act as extraction technique’s attribute, and the classifier is acted upon by Hybrid Support Vector Machine by achieving 91.23% accuracy.
引用
收藏
页码:1737 / 1762
页数:25
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