Classifying COVID-19-related hate Twitter users using deep neural networks with sentiment-based features and geopolitical factors
Classifying COVID-19-related hate Twitter users using deep neural networks with sentiment-based features and geopolitical factors
复制标题
使用具有基于情感的特征和地缘政治因素的深度神经网络对与 COVID-19 相关的仇恨 Twitter 用户进行分类
DOI：
10.1504/ijsss.2021.116373
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Xin Wang
中科院分区：
文献类型：
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作者：
P. Zhao;Xi Chen;Xin Wang
新冠肺炎大流行引发的反亚洲仇恨推文在美国和世界各地都是一个持续存在的社会问题。尽管已有的研究是通过使用文本分类器来完成的，但关于深度学习如何与公众的政治观点和地理多样性情绪一起工作，人们知之甚少。本文提供了一种新的方法来分类推特上与流行病相关的反亚洲人。这项研究创建了一个新的数据集，用于跟踪与大流行相关的Twitter用户，其中包含超过1000万条推文。目标用户通过识别他们对美国选举的情感和他们的地理位置来进行注释。实证结果表明，政治情绪和县级选举结果对模型构建有显著贡献。通过训练DNN模型，超过19万名Twitter用户被归类为仇恨或非仇恨，准确率为61%，AUC得分为0.63。
Anti-Asian hate tweets caused by COVID-19 pandemic is an ongoing social problem in the USA and around the world. Although existing studies have been done by using a text classifier, little is known on how deep learning works with public sentiments of political opinions and geographical diversities. This paper provides a new method to classify the pandemic-related anti-Asian hater on Twitter. A novel dataset for tracking pandemic-related Twitter users, which contains more than 10 million tweets, is created in this study. Target users are annotated by identifying their sentiments towards the US elections with their geolocations. The empirical result indicates that the political sentiments and the county-level election results make significant contributions to the model building. By training a DNN model, over 190,000 Twitter users are classified as hate or non-hate with a 61% accuracy and a 0.63 AUC score.