Abstract
Racism and physical attacks on Asian communities have spread in the U.S. and around the world. Xenophobia is a virus that may lead to an ongoing social problem in the post-pandemic era. Although existing studies have been done to classify anti-Asian haters, little is known on monitoring, tracking, and characterizing anti-Asian haters on social media platforms. In this chapter, a systematic examination of anti-Asian haters tracking and profiling methods is designed by using big data analytics with deep learning algorithms. Target haters are investigated and tracked by analyzing public opinions towards key topics in 2020, including the U.S. elections, stimulus checks, and economy opening strategies throughout data collection and preprocessing, text classification, sentiment analysis, data visualization, and association rule. Such a comprehensive study provides a variety of research opportunities in dealing with anti-Asian racism and xenophobia in and after the COVID-19 pandemic.
TopIntroduction
A tsunami of hate, xenophobia, and scapegoats has been evolved into one of the biggest challenges in and after the COVID-19 pandemic, not just throughout the United States, but around the world. As the outbreak of the novel coronavirus, millions of families have been harassed by a series of problems, such as health risks, financial distresses, security issues, and life uncertainties, whereas such recession exacerbates a tendency that the minority has been targeted from being possible hate. Due to an unproven speculation that as the COVID-19 starts from China, racism and crimes on Chinese and Asian communities have been dramatically increasing since the beginning of the pandemic. The tragedy in Georgia in regards to Atlanta spa shootings is one of the extreme manifestations in the form of fears of anti-Asian bias, while more racism and anti-Asian haters are invisible (Kao, 2021). Despite the announcement of the importance of protecting civil rights by lawmakers and FBI agencies, strong anxiety and negative emotions have been represented in terms of actions of discrimination, verbal abuse, and online harassment. Cyber-based anti-Asian voices, statements, and comments are everywhere on social media channels, such as Twitter, YouTube, Instagram, FaceBook, etc., along with the spreading of fake news and rumors.
Over the last few months, hatred incidents against Asian communities have quickly attracted scholars’ attention, from sociological, psychological, and socio-economic perspectives. Tessler et al. (2020) examined the historical antecedents that link infectious diseases and Asian Americans, which illustrated the anxiety of such a group of people suffered by hate crimes and negative biases during the COVID-19 pandemic. The psychological impact of anti-Asian stigma due to COVID-19 has been investigated and discussed by examining how it affects mental health and recovery, through implementing evidence-based stigma reduction initiatives, and via coordinating federal response to anti-Asian racism including investment in mental health services and community-based efforts (Misra et al., 2020). Meanwhile, Gover et al. (2020) believed that pandemic-related health crises have been associated with the stigmatization, which can be explained by exploring the reproduction of inequality. Xenophobia has spread much faster than the virus itself in the form of the socio-economic impact of COVID-19 by covering the important global rise in hate towards Asian ethnic origins (Cheng, 2020). Potential impacts of pandemic-related racial discrimination related to public health implications have also been investigated by citing relevant cases from the history of previous disasters in the U.S., along with exploring theoretical and empirical evidence (Chen et al., 2020). Existing studies in this field indicate that the anti-Asian hate violence associated with COVID-19 is not accidental, but has historical origins and is systematical and institutional.
However, the situation seems inevitably getting worse and worse as the COVID-19 related issues have been highly politicized. A large scale of unconfirmed and false information has been posted on social media, where people with different visions are being divided due to the pandemic associated with political reasons. Moreover, the situation became even more confusing and dangerous when the phenomenon of the pandemic-related anti-Asian encountered the 2020 U.S. election. Particularly, for some political purpose, the former President, Donald J. Trump, deliberately declared that COVID-19 is “China Virus”, whereas such an irresponsible remark has caused immeasurable harm to Asian communities (Masters-Waage et al., 2020). Nevertheless, provocative statements and voices have accelerated the division of the American people in the form of opposite opinions on anti-Asian racism, Black Lives Matter (BLM), stimulus checks, disease control measures, economy reopening strategies, vaccinations, and other topics related to the 2020 U.S. presidential election (Ho, 2021; Kim & Lee, 2021; Baccini et al., 2021; Yousefinaghani et al., 2021). The COVID-19 pandemic has been evolved into a political pandemic, which leads the world standing at a dangerous crossroads (Taskinsoy, 2020). This election season was unusual and sensitive when COVID-19 became one of the most important issues, combined with controversies in many other political sentiments beyond anti-Asian racism. These anti-Asian haters are homogenous in many characteristics due to the similar reactions and opinions on a specific topic of the 2020 presidential election.
Key Terms in this Chapter
Text Classification: A typical problem in information science that assigns a textual data to one or more classes or categories.
Social Media Analysis: A process of analyzing and tracking social media through related data collected from social network.
GIS: A system that can convert all types of data into a map to present all descriptive information and understand geographic context.
NLP: A processing method of computational linguistics for human language based on algorithms.
Web Scrapping: A process of extracting content information, such as texts, tables, images, from a certain website.
Sentiment Analysis: A process of analyzing and extracting the subjective opinions of texts.
Association Rule: A machine learning method that can find out correlative relationships from large-scale data.
Deep Learning: A broad family of machine learning models based on neural networks. Typical deep learning models are deep neural networks, convolutional neural networks, recurrent neural networks, deep belief networks, and deep reinforcement learning.