Improvement performance by using Machine learning algorithms for fake news detection

Eman Shekhan Hamsheen, Laith R.Flah
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Abstract

The prevalence of internet use and the volume of actual-time data created and shared on social media sites and applications have raised the risk of spreading harmful or misunderstanding content, engaging in unlawful activity, abusing others, and disseminating false information. As of today, some studies have been done on fake news recognition in the Kurdish language. For extremely resourced languages like Arabic, English, and other international languages, false news detection is a well-researched research subject. Less resourced languages, however, stay out of attention because there is no labeled fake corpus, no fact-checking website, and no access to NPL tools. This paper illustrates the process of identifying fake news, using two components of the dataset for fake news and actual news. Several classifiers were then applied to the quantity after using identifiers as a highlight of selection. Results of the proposed study demonstrated that Passive-Aggressive Classifier (PAC) outperformed the other classifiers on both datasets the dataset with an accuracy score of 93.0 percent and other classifiers were less in some percentage that show high accuracy as well since it is 90 percent.
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利用机器学习算法提高假新闻检测性能
互联网使用的普及以及社交媒体网站和应用程序上创建和共享的实时数据量增加了传播有害或误解内容、从事非法活动、滥用他人和传播虚假信息的风险。到今天为止,已经对库尔德语中的假新闻识别进行了一些研究。对于资源丰富的语言,如阿拉伯语、英语和其他国际语言,虚假新闻检测是一个研究得很好的研究课题。然而,资源较少的语言不受关注,因为没有标记的假语料库,没有事实检查网站,也无法访问NPL工具。本文阐述了识别假新闻的过程,使用假新闻和真实新闻数据集的两个组成部分。在使用标识符作为选择的亮点后,将几个分类器应用于数量。所提出的研究结果表明,被动攻击分类器(PAC)在两个数据集上的表现都优于其他分类器,数据集的准确率得分为93.0%,而其他分类器在某些百分比上也表现出较高的准确率,因为它是90%。
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发文量
64
审稿时长
8 weeks
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