Fake News Detection Using Intelligent Techniques

A. Vora, N. Shekokar
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引用次数: 3

Abstract

Nowadays fake news spreads very easily through internet and social media. People tend to easily believe in that fake information and start discussing about it. The more we hear about fake news, the more it becomes easier to believe on it. The main aim of fake news is to earn money through advertising revenue by web trafficking or discrediting a public figure, company,etc. Fake news is one of the biggest issues of this modern era especially in the world of social media. Lot of authors have contributed in detection of fake news using various machine learning algorithms but some gaps were found during analysis which are improved in our proposed model. Our approach detects fake news using intelligent techniques such as SVM, Naive Bayes and Logistic Regression. Their performance is analysed using parameters such as F1 score, recall, precision.support, accuracy.
利用智能技术检测假新闻
如今,假新闻很容易通过互联网和社交媒体传播。人们往往很容易相信虚假信息,并开始讨论它。我们听到的假新闻越多,就越容易相信它。假新闻的主要目的是通过网络交易或诋毁公众人物、公司等,通过广告收入赚钱。假新闻是当今时代最大的问题之一,尤其是在社交媒体世界。许多作者使用各种机器学习算法对假新闻的检测做出了贡献,但在分析过程中发现了一些空白,这些空白在我们提出的模型中得到了改进。我们的方法使用支持向量机、朴素贝叶斯和逻辑回归等智能技术检测假新闻。使用F1分数、召回率、准确率等参数对其性能进行分析。支持,准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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