机器学习分类算法检测covid - 19假新闻的实证评价

Q3 Computer Science

International Journal of Advances in Soft Computing and its Applications Pub Date : 2022-03-28 DOI:10.15849/ijasca.220328.04

Hiba Alsaidi, W. Etaiwi

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引用次数: 1

摘要

自covid - 19大流行开始以来，人类一直在与之抗争，不仅是为了保护自己的健康，也是为了抵消有关它的新闻和谣言。谣言和虚假指控几乎和病毒一样危险，因为它们会影响人们的心理健康，增加他们的压力水平。为了解决这个问题，可以使用几种机器学习技术来检测假新闻。在本文中，根据检测假新闻的能力，比较了四种不同的机器学习算法，包括朴素贝叶斯，决策树，支持向量机和逻辑回归。实验中使用了一个带注释的新闻数据集。实验结果表明Naïve Bayes在准确率、精密度、召回率和F1分数方面都优于其他算法。关键词:COVID-19，机器学习，假新闻检测

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Empirical Evaluation of Machine Learning Classification Algorithms for Detecting COVID19 Fake News

Abstract Humans have been fighting the Covid19 pandemic since it started, not just to protect their wellbeing but also to counteract the news and rumors that have been spreading about it. Rumors and false allegations can be almost as dangerous as the virus, as they affect people's mental health and increase their stress levels. To address this problem, several machine learning techniques could be used to detect fake news. In this paper, four different machine learning algorithms are compared according to their ability to detect fake news, including Naive Bayes, Decision Tree, Support Vector Machines, and Logistic Regression. A dataset of annotated news is used in the experiments. The experimental results show that Naïve Bayes outperforms other algorithms in terms of accuracy, precision, recall, and F1 score. Keywords: COVID-19, Machine Learning, Fake news detection.

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来源期刊

International Journal of Advances in Soft Computing and its Applications Computer Science-Computer Science Applications

CiteScore

3.30

自引率

0.00%

发文量

期刊介绍： The aim of this journal is to provide a lively forum for the communication of original research papers and timely review articles on Advances in Soft Computing and Its Applications. IJASCA will publish only articles of the highest quality. Submissions will be evaluated on their originality and significance. IJASCA invites submissions in all areas of Soft Computing and Its Applications. The scope of the journal includes, but is not limited to: √ Soft Computing Fundamental and Optimization √ Soft Computing for Big Data Era √ GPU Computing for Machine Learning √ Soft Computing Modeling for Perception and Spiritual Intelligence √ Soft Computing and Agents Technology √ Soft Computing in Computer Graphics √ Soft Computing and Pattern Recognition √ Soft Computing in Biomimetic Pattern Recognition √ Data mining for Social Network Data √ Spatial Data Mining & Information Retrieval √ Intelligent Software Agent Systems and Architectures √ Advanced Soft Computing and Multi-Objective Evolutionary Computation √ Perception-Based Intelligent Decision Systems √ Spiritual-Based Intelligent Systems √ Soft Computing in Industry ApplicationsOther issues related to the Advances of Soft Computing in various applications.