Fake News Detection Using Pos Tagging and Machine Learning

IF 1.1 Q3 CRIMINOLOGY & PENOLOGY Journal of Applied Security Research Pub Date : 2021-09-01 DOI:10.1080/19361610.2021.1963605
Afreen Kansal
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引用次数: 3

Abstract

Abstract In this digital era, one major concern is not knowing what news to believe and not to believe. With the ever-growing progress being made in social media and technology, the problem has become more prominent. This also played a very important role in spreading fake information in this pandemic, creating chaos and worry throughout the world. Through the paper, I propose to understand and analyze the underlying writing style that can help in detecting fake news before it can be published, using a style-based approach in detection. An ensemble machine learning classification model was tried out to detect fake news.
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基于位置标记和机器学习的假新闻检测
摘要在这个数字时代,一个主要的问题是不知道该相信和不该相信什么新闻。随着社交媒体和技术的不断进步,这个问题变得更加突出。这也在这场疫情中传播虚假信息方面发挥了非常重要的作用,在全世界制造了混乱和担忧。通过这篇论文,我建议在假新闻发布之前,使用基于风格的检测方法来理解和分析有助于检测假新闻的潜在写作风格。尝试了一种集成的机器学习分类模型来检测假新闻。
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来源期刊
Journal of Applied Security Research
Journal of Applied Security Research CRIMINOLOGY & PENOLOGY-
CiteScore
2.90
自引率
15.40%
发文量
35
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