Comparative analysis of machine learning algorithms to detect fake news

Sai Rama Krishna Indarapu, Jahnavi Komalla, Dheeraj Reddy Inugala, Gowtham Reddy Kota, Anjali Sanam
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引用次数: 3

Abstract

Fake news has immense impact in our modern society. The widespread dissemination of false news has the potential to have highly damaging consequences for both individuals and society. As the readers come across many fake news when they come across a real news, they believe that it could be another fake news. The aim of this project is to perform a comparative analysis of three algorithms (Multinomial Naive Bayes, Passive Aggressive Classifier and Decision Tree Classifier) using Natural Language Processing techniques to develop a solution that users can use to identify false or misleading information. As Passive Aggressive Classifier gave the best results, prediction is done using this classifier.
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机器学习算法检测假新闻的比较分析
假新闻对我们的现代社会有着巨大的影响。虚假新闻的广泛传播有可能对个人和社会造成极具破坏性的后果。当读者看到一条真实的新闻时,他们会遇到很多假新闻,他们认为这可能是另一条假新闻。该项目的目的是使用自然语言处理技术对三种算法(多项朴素贝叶斯,被动攻击分类器和决策树分类器)进行比较分析,以开发用户可以用来识别虚假或误导性信息的解决方案。由于被动攻击分类器给出了最好的预测结果,因此使用该分类器进行预测。
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