As the COVID-19 pandemic has unfolded, 1 many local authorities have locked down cities through social distancing orders to slow down the speed of virus transmission. Consequently, daily routines and social norms have changed and social media platforms, such as Twitter, have emerged as one of the primary places where people create and exchange information relating to the virus.
Although people are physically distant, the information they have shared through social media streams during this global health and information crisis is influential (Xie et al., 2020) . Some of the posts and commentaries being shared online, however, reflect or even amplify contemporary social stigmatization, discrimination and outright Hate Speech, especially directed at China and the Chinese people. To understand their potential to influence public opinion and behavior, these controversial tweets (Twitter posts) need to be documented and analyzed in a timely manner. In early February 2020, therefore, we began capturing and analyzing tweets related to the coronavirus and China, with a specific focus on Hate Speech.
In this paper, we present an initial analysis of the resulting archive of tweets together with the analytical methods we have used. Our broader goal has been to examine the role that an archive could play in informing the public, educating the society, and preventing or smoothing future crises (Chew & Eysenbach, 2010) . Furthermore, we are methodologically interested in identifying effective processes, from archiving to analyzing, that can be used in real time during a rapidly evolving crisis of this magnitude.
2 | STUDY DATA AND METHODS 2.1 | Data collection 2.1.1 | Building a database of tweets
We have been documenting tweets related to China since the start of the COVID-19 pandemic. "China" and "Coronavirus" are words that have been associated in the public mind ever since the Chinese authorities informed the World Health Organization (WHO)'s China office of the occurrence of pneumonia cases with unknown cause in Wuhan, China on December 31, 2019 (Ravelo & Jerving, 2020) . During the critical period 2 between when the coronavirus emerged to the ensuing outbreaks, we have queried the Twitter API using "china+and+coronavirus" and have collected 3,457,402 English-language tweets (key tweets).
We have also been obtaining coronavirus cases information in the U.S. from the dataset provided by John Hopkins University. 3 To further detect whether there might be any systematic trend in Hate Speech within groups of states that share certain characteristics such as demographic composition, we have extracted supplementary information from the U.S. Census Bureau, 4 the U.S. Bureau of Labor Statistics 5 and the Federal Election Commission (Eileen & Leamon, 2017) .
In this project, we utilize data visualization as an exploratory research method and have been able to effectively identify patterns and correlation among variables (e.g., new cases, number of hate tweets) by using barchart, gradient maps and word cloud.
Word count tables and Word Clouds are straightforward representations of discourse. We have generated the word cloud in Figure 1 as a demonstration of the overall trend of tweets according to their hashtags, creating a summary of user input topics.
Among the top frequently used hashtags in the archive, several main categories can be discerned, including location, person, organization, and abstract concept. One clear trend is the occurrence of locations, as shown in Table 1 and Figure A1 in Appendix III. "wuhan", with more than 24,000 occurrences, is the top hashtag used in this archive. Other locations used as hashtags include Italy, USA, Hong Kong, and Hubei (of which Wuhan is the capital). This preliminary finding confirms our intuition that analyzing the trend and speech discourse together with demographics could be productive.
In terms of contextual information, two of the frequently used hashtags, "chinesevirus" and "wuhanvirus", are associated with discriminatory comments. Both hashtags are violations of the WHO's convention for naming new human infectious diseases (WHO, 2015) . This preliminary finding suggests that Twitter users have often used discriminatory speech regarding specific groups, thus leading us to conduct further investigation using lexicon-based information extraction methods. Figure 2 provides an aggregated histogram 6 and line plot that analyze the relationship between the number of new cases versus the number of key tweets per day. We can observe an overall increasing trend for both the number of new cases and the number of key tweets over time. Interestingly, we can also observe a substantial surge in the number of key tweets from March 16 to March 19. We believe this occurrence might be caused by the burgeoning of confirmed cases in the U.S. and increasing media attention.
After removing stop words and ignoring non-textural strings, we use lexicon-based methods for the primary data wrangling of both Hate Speech detection and aspectbased emotion scoring. For Hate Speech detection, we use the Hatebase 7 dictionary, which includes thousands of discriminatory words. For emotion extraction, we use an emotion lexicon created using crowdsourcing with Mechanical Turk (Mohammad & Turney, 2010) .
For each tweet, we tabulate the number of words related to each element in the emotion dictionary and then generate 10 variables that include sentiments (negative, positive) and emotions (anger, anticipation, disgust, fear, joy, sadness, surprise, trust) and dividing them by the tweet's word count to normalize. For tweets with words defined as hatewords, we label them as "Hate Speech". 8 To identify location information, we use the carmen Python package based on geocoding tools (Dredze, Paul, Bergsma, & Tran, 2013) . We extracted location information for 1,401,374 tweets from the archive and found that 40.53% of them are from the U.S. 9 Finally, we average the normalized sentiment and emotion variables by date and state to create the aggregated datasets. Each dataset also contains the total number of key tweets and the percentage of Hate Speech on the aggregated levels. For the state-level dataset, we further introduce characteristic variables PoliticalParty, AsianPercentage, UnemploymentRate, and PovertyRate. 10 2.4 | Statistical methods
Decision tree is a widely used machine learning technique that uses a tree-like model for making decisions and analyzing possible consequences. The target of building a decision tree is to minimize the entropy of each leaf so that it contains either only Hate Speech or Non-Hate Speech. Using Gini Impurity, 11 the decision tree model could also return the importance of features, from which we can identify the best Hate Speech indicator.
Another way to analyze and visualize Hate Speech behavior across each state is to use network methods. We can consider states to be nodes in a network and use methods such as modularity and centrality to evaluate the network. Network modularity is a measurement to show network homophily because similar nodes tend to connect with each other. If we assign groups to the states using some criterion (e.g., poverty), we can calculate the modularity of the network to show whether under such a criterion, states that are similar have homogeneity in Hate Speech behavior. Network centrality is a measurement to describe node property. Different centrality 3 | RESULTS
To better understand the relationship between emotions and Hate Speech, we aggregate the negative sentiments 12 and observe a similar trend in the negative sentiment score and the number of Hate Speech over time, as shown in Figure 3 .
To further analyze the relationship between emotions and Hate Speech, we implement a binary decision tree classifier to create a model that can infer whether a tweet is Hate Speech (negative class stands for Hate Speech). The decision tree model is built based on the eight emotion features in 3,461,929 positive samples and 25,467 negative samples (Train Set).
Based on Table 2 and Table 3 , we believe that with a 100% specificity, our decision tree classifier could successfully detect all Hate Speech defined by the lexicon-based approach. With similar precision, our model's prediction of a tweet belonging to Non-Hate Speech is definite. Our negative predictive value is 87.87%, thus we can conclude that our model is proficient in detecting Hate Speech. Figure A2 in Appendix III provides an overview of the generated tree, from which we can also obtain the contributions of features in detecting Hate Speech.
In Table 4 , based on the decision tree, we extract the feature importance of each emotion and conclude that surprise and fear are the two important Hate Speech indicators. As shown in Figure A2 in the Appendix IV, although the root node uses joy as a splitting criterion, it F I G U R E 2 Number of new cases in the U.S. and Number of tweets per day in the U.S makes little contribution to finding Hate Speech. 13 The actual nodes that contribute the most to detecting Hate Speech are shown in Figure 4; According to the steps and conditions 14 specified in Figure 6a , we visualize the resulting network in Figure 6b .
The modularity of our network, Q, indicates whether our grouping criteria, for example state poverty rate, are informative in revealing each state's Hate Speech behavior: 
where e r and a i 15 are the proportion and the expected proportion of similar states pairs 16 within each group. If Q > 0, the similarity of states within groups is comparatively larger than that of between groups, which indicates that our method of splitting the groups is informative in revealing each state's different Hate Speech behavior. We analyze the effects of four grouping criteria on Hate Speech behavior: political party, poverty rate, Asian percentage, and unemployment rate. For political party, we group the states by Republican states and Democratic states according to the 2016 Presidential Election Results. For the other factors, we group all the states into quadrisections. According to the division, we calculate the modularity of each criterion (Table 6 ). We conclude that the poverty rate and the unemployment rate of each state are informative factors in studying the Hate Speech behavior of each state.
Centrality of a network is a measurement to describe the importance of each node. In our case, we are interested 
where A means the adjacency matrix; 1 means the all-one vector; jVj means the number of nodes. In our case, higher degree centrality means the state's Hate Speech behavior is common among all states. Otherwise, the state may have a unique Hate Speech behavior (either too high or too low). Compared with degree centrality, page rank centrality, σ P , gives high centrality to nodes that connected with high centrality notes, that is page rank centrality groups "potentially" similar states in a more extreme way. Therefore, for those states that have low page rank centrality, their Hate Speech behaviors have higher levels of distinctions. For the centrality of each state:
where A refers to the adjacency matrix, k j refers to the degree of node j, and α, β are constants. From Figure 7a ,b we can discern the states that perform "uniquely" in Hate Speech. We can observe that California, New York, Florida, Texas, Pennsylvania, Illinois (from degree centrality), and Massachusetts, Rhode Island, North Dakota (from page rank centrality) have low centrality compared with other states. Combined with Figure 5 , we observe that those states either have extremely high confirmed cases (such as New York, Massachusetts, Illinois, California) or have extremely low confirmed cases (such as North Dakota). Therefore, further study might usefully compare the Hate Speech behaviors of different states.
We observe that many hashtags, the user-defined topic of tweets, include inappropriate naming conventions and discriminate against certain ethnic or geographic groups. Moreover, by taking the U.S. as an example group of English language Twitter users, we find associations between the amount of Hate Speech and sentiments/state demographic factors, where surprise, fear, poverty and unemployment rates are of greater importance. These initial findings provide us with a base for more focused investigation, ideally associating our dataset with news during this period.
It is also important to reiterate that this tweet archive and the related analyses are not purely historical in nature or importance. They have a central role to play in making rational voices heard by the public and raising awareness about social and humanistic issues during crises. It is our hope that the knowledge we gain and the methodological approaches we develop will be helpful also to other researchers working to anticipate and mitigate social tensions during global crises.
We realize that our dataset could be a good complement to the "COVID-19-TweetIDs" dataset, which has a more general scope (Chen, Lerman, and Ferrara, 2020) . We also notice that scholars from various fields are analyzing COVID-19-related Twitter datasets by using topic modeling methods and characterizing individual user behaviors (Chen, Yang, et al., 2020) . Compared to these investigations, our scope is more comprehensive, encompassing archival, analytical, and methodological processes, and including utilizing machine learning and network methods for visualization and analysis.
With the Twitter API's limitations 17 on continuity of searching, we have several data gaps. Since we either normalize the variables or analyze correlations, we expect this discontinuity to have minimal influence on our analysis. While the number of key tweets quickly drops then slowly increases after March 19th, this drop may be due to delayed adoption of the term "COVID-19" in place of "coronavirus", thus potentially diverting much of the information stream.
Restricted by the scope of the dictionaries, lexicon-based methods likely underestimate the sentiment/emotion score by failing to identify malformed words and colloquial expressions (e.g., gr8, RIP) (Taboada, Brooke, Tofiloski, Voll, & Stede, 2011) . Itis also not sufficiently sophisticated to capture overall context and complex syntax scenarios (e.g., ironic language use). To combat these limitations and extract more precise indicators for Hate Speech, we plan to create a manually labelled dataset and employ other supervised learning models such as a support vector machine.
is not surprising. Because most of the tweets in this archive are in English and most of the users are located in the U.S., we associate the number of tweets with the number of new cases in the U.S. 7 https://hatebase.org/ 8 Examples of Hate Speech are presented in Appendix I Table A1 9 Some unincorporated territories of the U.S. such as Guam and USVI are dropped due to insufficient sample size. 10 Descriptive results for the state level dataset are presented in Appendix II Table A2 11 For a set of items with J classes, where p i is the fraction of items labeled with class iin the set, the Gini Impurity of the class is I G p ð Þ = 1− P J i = 1 p 2 i . The decision tree algorithm chooses one feature that could minimize Gini Impurity and uses it as the decision criterion. 12 Fear, Anger, and Disgust could be directed at haters as well as Hate Speech, and Sad indicates negative emotions which are related but not limited to hate. For the convince of the first and overall result, we aggregate them together, while distinguish them in later analyses. 13 The Joy node only separates out a large amount of Non-Hate Speech (the left branch), and a significant amount of Non-Hate Speech remain unidentified. 14 We normalize the number of Hate Speech tweets by dividing them into the population of each state/district. 15 Here, e r = 1 2m P n i = 1 P n j = 1 A ij δ g i ,r δ g j ,r and a r = 1 2m P n i = 1 k i δ g i ,r ,where mstands for the number of edges, rstands for the number of groups, Astands for the adjacency matrix, δ ij stands for the Kronecker delta, and k i stands for the degree of node i; g i stands for group i. 