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An Ensemble Machine Learning Approach to Understanding the Effect of a Global Pandemic on Twitter Users’ Attitudes
International Journal of Computers Communications & Control ( IF 2.7 ) Pub Date : 2021-03-17 , DOI: 10.15837/ijccc.2021.2.4207
Bokang Jia , Domnica Dzitac , Samridha Shrestha , Komiljon Turdaliev , Nurgazy Seidaliev




It is thought that the COVID-19 outbreak has significantly fuelled racism and discrimination, especially towards Asian individuals[10]. In order to test this hypothesis, in this paper, we build upon existing work in order to classify racist tweets before and after COVID-19 was declared a global pandemic. To overcome the difficult linguistic and unbalanced nature of the classification task, we combine an ensemble of machine learning techniques such as a Linear Support Vector Classifiers, Logistic Regression models, and Deep Neural Networks. We fill the gap in existing literature by (1) using a combined Machine Learning approach to understand the effect of COVID-19 on Twitter users’ attitudes and by (2) improving on the performance of automatic racism detectors. Here we show that there has not been a sharp increase in racism towards Asian people on Twitter and that users that posted racist Tweets before the pandemic are prone to post an approximately equal amount during the outbreak. Previous research on racism and other virus outbreaks suggests that racism towards communities associated with the region of the origin of the virus is not exclusively attributed to the outbreak but rather it is a continued symptom of deep-rooted biases towards minorities[13]. Our research supports these previous findings. We conclude that the COVID-19 outbreak is an additional outlet to discriminate against Asian people, instead of it being the main cause.




中文翻译:

一种集成的机器学习方法,用于了解全球大流行对Twitter用户态度的影响




人们认为,COVID-19疫情极大地加剧了种族主义和歧视,尤其是对亚洲人的种族歧视[10]。为了检验该假设,在本文中,我们基于现有工作对COVID-19被宣布为全球大流行之前和之后的种族主义推文进行分类。为了克服分类任务的困难语言和不平衡性质,我们结合了一系列机器学习技术,例如线性支持向量分类器,Logistic回归模型和深度神经网络。通过(1)使用组合的机器学习方法来了解COVID-19对Twitter用户态度的影响,以及(2)改进自动种族检测器的性能,我们填补了现有文献中的空白。在这里,我们表明,在Twitter上针对亚洲人的种族主义并没有急剧增加,在大流行之前发布种族主义推文的用户在疫情爆发期间倾向于发布大约相同数量的信息。先前关于种族主义和其他病毒暴发的研究表明,对与病毒起源地区有关的社区的种族主义并不仅仅归因于暴发,而是对少数群体根深蒂固的偏见的持续征兆[13]。我们的研究支持这些先前的发现。我们得出的结论是,COVID-19疫情是歧视亚洲人的另外一个渠道,而不是主要原因。先前关于种族主义和其他病毒暴发的研究表明,对与病毒起源地区有关的社区的种族主义并不仅仅归因于暴发,而是对少数群体根深蒂固的偏见的持续征兆[13]。我们的研究支持这些先前的发现。我们得出的结论是,COVID-19疫情是歧视亚洲人的另外一个渠道,而不是主要原因。先前关于种族主义和其他病毒暴发的研究表明,对与病毒起源地区有关的社区的种族主义并不仅仅归因于暴发,而是对少数群体根深蒂固的偏见的持续征兆[13]。我们的研究支持这些先前的发现。我们得出的结论是,COVID-19疫情是歧视亚洲人的另外一个渠道,而不是主要原因。


更新日期:2021-04-01
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