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A corpus-assisted discourse analysis of Taiwanese students’ sentiments toward Asianphobia on the news amid Covid-19

The Covid-19 pandemic has fuelled the latest surge of anti-Asian hate. Raising Taiwanese university students’ awareness about the dangers of racism has never been more important. This study draws on a class activity about media discourse, particularly on the representations of xenophobia and other forms of intolerance toward Asians during the pandemic. The self-created corpus was based on the students’ collected news articles (119,375 words) and their feedback on the topic (14,641 words). The theoretical framework grounded on corpus-driven discourse analysis was used to investigate linguistic evidence of exclusion, discrimination, and racist discourse against the Asians and Chinese. This research also set out to understand the Taiwanese students’ attitudes and concerns about the discourse circulated in the media. The web-based text analysis program Voyant Tools was employed to identify keywords, frequency, collocations, and word patterns in context. The findings revealed a high occurrence of the labels, Chinese virus, kung flu, Asian virus, and yellow peril– framing representations of the Chinese and Asians which spiked up during the pandemic. These epithets further mitigate the Chinese as the source of the virus. The repetitive use of such referencing stigmatizes the Asian community, and further constructs the notion of in-/out- group where Asians make up the out group– the ones to be avoided. The findings also showed the students’ strong sense of national pride echoing the slogan “ Taiwan can help, ” calling themselves Taiwanese , and categorizing themselves as Asian , but disassociating from the “ Chinese mainlanders .” This study provides an insight to the Taiwanese’s perception of identity, where racial dilemmas run deep as they are ethnically related, historically, and geographically connected. What lies underneath is a sense of fear for their own safety as they carry their race on their face.


Introduction
"I don't want your coronavirus in my country," said one of the two assailants as they kicked and beat up the 23-year-old Jonathan Mok, Singaporean, and a student at University College London ("Coronavirus: Teens arrested," 2020, para. 1).
"Take off your mask," the African American woman shouted as she attacked a Taiwanese graduate student in New York; so traumatized after this incident that the student "considered moving back to Taiwan" (NOWnews, 2021, para. 8).
Stories like the ones above are only some of the Covid-19-related violent hate crimes against Asians that were reported on the news. As the number of criminal cases spiked, so was the gravity and seriousness of the attacks--from punching, shoving, and kicking to stabbing and shooting (Carbal, 2021). The heinousness of these racially provoked crimes was so reprehensible that it has become deadlier than the virus itself. Asianphobia and the construction of anti-Chinese sentiments that were reported by the media was predominantly instigated by the fact that Covid-19 was first identified in Wuhan, China. As the virus spreads from the east to the west, the propensity to blame the nuances of the pandemic to China and to anything or anyone related to being Chinese has also increased. As Taiwanese students tend to travel abroad either for education or leisure purposes, the current anti-Chinese hostility puts them in a very vulnerable position as many of them bear physical resemblance to the Chinese from mainland China. The need to raise students' awareness about racism in the news has become more relevant than ever.
This study is grounded on a corpus-assisted discourse analysis that was undertaken to investigate the students' sentiments on racist discourse in the news surrounding Covid-19. The analysis was predicated on a self-created corpus drawn from the students' written responses. Critical discourse analysis based on corpus linguistics (Baker et al., 2008;Biber et al., 1998) using Voyant Tools (Sinclair & Rockwell, 2016), a web-based reading and analysis program for digital texts, was conducted to investigate the correlational impact of the word patterns and other linguistic devices. These results were then interpreted following Fairclough's (1995Fairclough's ( , 2001) discourse analysis model, van Dijk's (1984van Dijk's ( , 2015 discourse analysis of discrimination, and Wodak (2001) negative presentations to evaluate the racist discourse, students' attitudes, and social representations.

Literature Review
The main aim of CDA with the corpus linguistic approach used in this study as framework was to analyse the lexical choices made by the students concerning the issues surrounding the media coverage of Covid19 and the racist labels used on the news reports. The interplay of language, power, and ideology is inevitable when looking at racist discourse. Lexical items, collocations, and how they are constructed in a phrase defines the contexts that expose a certain social ideology encapsulated in the discourse that has potential influence in shifting people's attitudes and behaviour (Fairclough, 1995). Studies on CDA emerged in the 1970s, which concentrated mostly in the UK and Australia; topics of discourse revolved around racism, sexism, and politics (Van Dijk, 1993, 2015 however, the variety of issues expanded to other cultural contexts, traditions, and genre (Machin & Van Leeuwen, 2007;Wood & Kroger, 1995;Schmutz, 2009). Fairclough (1995) proposed a three-dimensional model (see Figure 1) as a theoretical framework for examining discourse, which begins with a "linguistic description of the language text, interpretation of the relationship between the (productive and interpretative) discursive processes and the text, and explanation of the relationship between the discursive process and the social processes" (p. 97).

Figure 1 Fairclough's three-dimensional method
Text analysis begins with a description of text (written or spoken) and generation of text genres, types, and categories. As the analysis moves from the literal and obvious, a deeper analysis entails an interpretation of the meaning derived from the description of the text and its discursive production, that is, how it is (re)produced and utilized, accepted, and manipulated. Further explanation requires examining the social context in which the (re)production and interpretation of the text are socio-culturally situated, the conditions of that environment, and what hidden ideology they may carry. For CDA experts, criticality in discourse analysis goes beyond sketching the social processes and structures, but they also argue that discourse itself is not merely an outcome as it also reinforces the (re)production of these social processes (van Dijk, 1993;Fowler, 1991).
Similar critical approach to analysing racism in the media was purported by Hartmann and Husband (1974) based on their study of black racism in the UK; they suggest that racial prejudice views are not only a result of misinformation and that "prejudice attitudes cannot be changed significantly, independently of the structural relationships to which they relate" (p. 41). Therefore, racism is not an individual pursuit but rather a discursive social practice. Van Dijk (1998) strongly argues that social factor plays an intentional course of action to shelter the interests of the in-group or those who belong to a self-perceived social cluster that differentiates them from the others. He posits that racism and racist discourse are deemed crucial in the preservation of power dynamics and social structural relationships that have been imposed or even deliberately emplaced in society, as there is the need for "rationalization and justification of discriminatory acts against minority groups" (van Dijk 1984, p.13). He categorized them as "the 7 Ds of Discrimination: dominance, differentiation, distance, diffusion, diversion, depersonalization or destruction, and daily discrimination" (van Dijk 1984, p. 40). These are strategies that lay out the demarcation line between the other, for instance, concepts that portray "positive self-presentation" and "negative other-presentation", exclusion from events or social activities, ruining or destruction of image or properties, assassination, or manslaughter (p. 40). Wodak (2001) also noted five discursive strategies of positive self-presentation and negative other representation in relation to discourse of identity and difference ( Figure 2). These strategies are a "systematic way of using language" which can be identified as "intentional... discursive practices adopted to achieve a particular social, political, psychological or linguistic aim" (Wodak, 2001, p. 73).

Figure 2
Strategies of positive self-presentation and negative other presentation (adapted from Wodak, 2001) The media play a crucial role as gatekeepers and agenda-setters of hegemony and how the other is represented or framed, thus perpetuating unequal social relationships between social groups or communities, as Cottle (2000, p. 2) postulates "to construct a sense of who 'we' are in relation to who 'we' are not, whether as 'us' and 'them'," The way these groups are (re)presented is only a snippet of the context, and this what Entman (1993) refers to as framing which he defines as the act of "select[ing] some aspects of a perceived reality and make them more salient in a communicating text" (p. 52). Tewksbury (2015) describes framing in the news or by the media/press as "the verbal and visual information in an article that directly or implicitly suggests what the problem is about, how it can be addressed, and who is responsible for creating and solving it" (para. Introduction). For linguists, text analysis in media discourse encapsulates the analysis of discourse structure, linguistic features, and their functions in delivering and/or maintaining certain ideology. Thus, seeing the evolution of the discourse, from the time a news story comes out to the time it is consumed and (re)produced by the people, can be an eye-opener that can trigger further debates and discussions.

Studies employing corpus linguistics and discourse analysis
Blending corpus linguistics (CL) and critical discourse analysis (CDA) has been adopted by other renowned experts in the field (Partington, 2006;Fairclough, 2009;Cheng & Lam, 2013). As corpus driven CDA is being used as an analytical approach, Baker et al. (2008) suggest that achieving a balance between these two approaches is essential. Because of the quantitative nature of collecting and analysing the texts used in corpus linguistics (Sinclair, 1991), it is deemed as highly objective compared to the subjective interpretation applied to semantic and pragmatic content analysis when performing CDA (Fowler, 1991). By integrating CL into CDA, it strengthens the analysis using sampling and a large collection of text where textual features can be analysed to construct reliable generalizations about the lexical terms that are being studied (Stubbs, 1997). The arduous task is to explore the lexical choices made and probe into how they were used to express social experiences or processes (Cheng, 2012) and their overall influence on the people or vice versa.
Aside from the studies of the experts mentioned above, there are a few recent studies on news media discourse using a corpus-driven approach to CDA. Baker, Gabrielatos, and McEnery (2013) published a paper on the corpus-driven analysis of representations of Muslims by the British press from 1998-2009. They found that Muslim and conflict were correlated and the implicit connotations that they were often in conflict with non-Muslims. The suggested legitimation strategies to include other representations. Cheng and Lam's (2013) study looked at western perceptions of Hong Kong ten years after it was handed back to China in 1997. Their observations using Concgram and Wmatrix analytical tools suggest that the West have changed their views toward Hong Kong, which was a stark difference from the negative perceptions that other researchers have claimed. Their paper provided a re-examination of the struggle to gain balance in relations and power dynamics between the West and China.
Taylor's (2014) research was a cross-linguistic corpus-assisted discourse analysis of how migrants are textually represented in the UK and Italian press. Her research found various nationalities that occurred frequently with the lexical items refugee, asylum seeker, immigrant, and migrant considering the distinctive historical accounts of both countries concerning immigration and emigration. Since she was analysing two languages at the same time, she posits that it is paramount for a researcher to consider the nuisances of translation and understanding language in context when performing cross-linguistic studies.

Most recent studies on CDA and Covid-19
In view of the specific context (Covid times) in which racism is being put under scrutiny, there were a few studies that have been recently published. For instance, AlAfnan's (2020) study examined media bias, ideology, and dominance that existed in Chinese and American newspapers (American Washington Post newspaper and the Chinese People's Daily) surrounding Covid-19 as the foreign virus. The biases he found were mostly in the context of gatekeeping, coverage, and statement bias, which reflected ideological influences particularly in the selection of topics and the tone of reporting. He concludes by saying that foregrounding can be both explicit and implicit and suggests that the interpretation depends on the readers' adeptness with the current events between these economic powers.
Another recently published study was Fatima's (2020) paper on the construction of journalistic frames and interpretation of editorial coverage of Covid-19 in New York Times. She found that the New York Times' editorial reporting of Covid-19 was generally constructive with occasional negative framing in a few published coverages. Negative frames were mainly attributional to finding faults, blaming the repercussions of the conflict on others; the latter was more dominant in comparison to raising solidarity.
The article published by Cotik, Debandi, Luque, Miguel, Moro, Perez, Serrati, Zajak, and Zayat (2020) was based on a research in progress with a very short timeline, which was akin to the other papers mentioned above. Cotik et al.'s work looked at hate speech in Spanish tweets (Twitter short messages) related to the newspaper articles concerning Covid-19. Their findings suggest a strong feeling of hate against the Chinese people. Like Taylor's (2014) proposition, they also recommend using translations, and it is inevitable when dealing with textual data in foreign languages.

Studies on using student feedback as corpus
There are limited studies on CDA using student feedback as data for the corpus; some of these studies are presented below.
Crosthwaite, Storch, and Schweinberger's research (2020) investigated the impact of written corrective feedback (WCF) provided on second language (L2) writing in corpus consultation for determining L2 errors. Hyatt's (2005) study on critical genre analysis of a corpus based on the feedback commentaries on graduate students' assignments provides insights on the different practices of providing feedback. He provided suggestions for critical inclusion of student-writers' contributions to the academic discourse which would lessen the power imbalance brought about by the conventions of prescriptivist's views.
For studies conducted in Taiwanese context, there are even fewer studies. Most of them focus on the application of corpus tools in enhancing Taiwanese students' language learning, for example, EFL academic writing (Reynolds, 2016), error detection and revision for Spanish learners in Taiwan (Lu, Chu, and Chang, 2013). Sun's (2000) also published a paper on using online corpus to facilitate language learning where she also administered a questionnaire in gathering students' feedback. Yeh's (2019) study on problematizing Taiwanese students' perceived sentiments on the delivery of online video conferences for intercultural communication courses drew her findings from a self-created corpus using students' written feedback on the course. The general findings of her study suggest that student presentations could be more interesting if there were more opportunities for them to "talk about themselves and to hear their views on different English varieties and accents, and what all that means to them" (p.19).
This study will certainly fill the gap in research as it highlights the Taiwanese students' perceptions toward Covid-19-related racist discourse in the News, and the discrimination and bigoted actions toward anyone who looks Asian Chinese.

Defining Asians
This section will provide some definitions of the term Asian for the sake of clarity on terminology and usage to avoid confusion. The Oxford Dictionary (2021) categorizes Asian as a noun and defines it as "a person from Asia, or whose family originally come from Asia." Oxford provided further explanation to clarify the racial distinction depending on which English variety one is using: In British English Asian is used especially to refer to people from India, Pakistan and Bangladesh. In North American English it is used especially to refer to people from the Far East. (Oxford Dictionary, 2021) As an adjective, however, Oxford simply defines and refers to it as anyone or anything "connected with Asia" (2021). However, a Google search of Who counts as Asian? and Who are Asians? leads to more racial profiling that is mostly centred to the Asian-American population. According to the U.S.
Census, Asians are the fastest growing racial group in the country (2021), and these are people "having origins in any of the original peoples of the Far East, Southeast Asia, or the Indian subcontinent including, for example, Cambodia, China, India, Japan, Korea, Malaysia, Pakistan, the Philippine Islands, Thailand, and Vietnam." (Kambhampaty, 2020, para. 2). They are grouped together by government classifications under an aggregate reference-Asian American and Pacific Islander (AAPI) (U.S. Census, 2021). However, race and ethnic divisions lie within them as Lee and Ramakrishnan (2017) posit, Asian Americans "draw a sharp boundary between Asian and non-Asian that separates East Asians (Chinese, Korean, and Japanese) from South Asians (Indians, Pakistanis, and Bangladeshis) and, to a lesser extent, Southeast Asians like Filipinos." (Para. 9, emphasis added).
Nonetheless, when referring to Asianphobia or hate crimes and violence towards Asians, the media tend to use the term loosely, and for many "the default for Asian is East Asians" (Wang & Ramakrishnan, 2017, para. 1). Nonetheless, Lee and Ramakrishnan (2017) argue that this default categorization of Asians and the racial divide within the Asian community create fault lines that make them even more vulnerable to xenophobic attacks. Lee and Ramakrishnan (2021) call for solidarity because for them their experiences of discrimination are one that they all have in common, and that "the threat of being victimized by anti-Asian hate affects all Asian Americans" (para. 13).
What is disquieting though is that the current anti-Asian rhetoric does not only happen in the U.S. As Martin and Yoon (2021) report, "[S]pikes in harassment and violence against Asians trigger reckonings in Europe, Australia, Canada and elsewhere" (para. 1). For example, there have been accounts of increased attacks on Covid-19-related racism in the U.K. ever since the country went on lockdown in May 2020 (Haynes, 2021), and in Australia where the police adopted a "new national anti-racism framework to address prejudice against the Asian community due to COVID-19" (ibid., para. 9). Peterson (cited in Haynes, 2021) argues that the global anti-Asian violence is a "collective trauma", and that "[I]t also has a wider ripple impact on the Asian diaspora worldwide…" (para. 1).
In other words, Asian hate is not just an American problem; it has become a reckoning force that all Asians in the world share and need to rally against.
As most of the international news reports included in this study used "Asians" in reference to Asian hate aimed at East Asians, that is, Chinese, and the fact that there were reports on related incidents across the globe, the disparity between Asians and ethnic Chinese seems to lie on a thin line of racial assignment. Hence, it is for that reason that the terms Asians, Chinese, and Asian Chinese will be used interchangeably throughout the paper.

Research questions
This study is guided by the following research questions: 1. Based on the students' collected news reports covering Covid-19, what are the linguistic evidence(s) of exclusion, discrimination, and racist discourse against the Asians and Chinese? 2. What are the Taiwanese students' attitudes and concerns about the discourse circulated in the media, and how are they affected by it? 3. What are the Taiwanese students' suggestions for resolutions to the conflict?

Method
For this study, the corpus assisted CDA approach was used in analysing the content of the students' feedback. Lexical choices, content analysis, quotations and labels used in relation to racist discourse against the Chinese during Covid-19 were applied. Data were collected using a blend of quantitative and qualitative approaches, which according to Baker et al. (2008) is a "useful methodological synergy" (p. 273) and it also reduces researcher bias. Corpus linguistics and CDA allow both approaches to take place. A self-created corpus (Feedback Corpus) was drawn from the students' written responses (45 articles) with 14,641 total words and 2,019 unique word forms. A sub-corpus was also created to group the students' feedback into three categories: Labels, Conflicts, and Resolutions. In the feedback report, students were asked to construe the characterization of anti-Chinese views as conflicts, how they think they might be affected by them, and what resolutions they can offer to alleviate the problem and avoid being victims of racial slurs and verbal attacks. They made their own inferences concerning these conflicts in relation to cross-cultural communication scenarios that they might experience in the future. This paper presents the outcome of the report which provides an insight to the students' concerns about this issue. Critical discourse analysis based on corpus linguistics (Baker, et al., 2008;Biber et al., 1998) using Voyant Tools (Sinclair & Rockwell, 2016), a web-based reading and analysis program for digital texts, was conducted to investigate word patterns that rose from collocations and concordance data and the overall correlational impact of these linguistic devices.
Following Fairclough's (1995Fairclough's ( , 2001) discourse analysis model, I looked at the text and how they are linguistically presented and described in the reports. Part of interpreting the texts is to critically scrutinize the relationship between how the texts are produced (written) and dig beneath the literal meaning to uncover the hidden messages, the discursive pragmatic message(s) and social ideologies that are being set forward. The results were then interpreted using critical discourse analysis (CDA) theoretical framework of van Dijk (1984) on discourse and discrimination and Wodak's negative (re)presentations (2001) to evaluate the racist discourse in the news, students' attitudes, and their mental and social representations that they share.

Context
Before analysing the students' written feedback, the following description of the in-class workshop on cross-cultural communications and media discourse should provide a brief background of the learning context. The course Cross-cultural Communications is a required course for 4th year undergraduate students (n= 45) enrolled in the English program offered at a private university in Taiwan. They were all Taiwanese, between the ages of 20-22. In addition to lectures, in-class tasks and hands-on workshops were conducted to help students understand the theoretical underpinnings of various cultural dimensions and social realities of prejudice and discrimination. These pragmatic notions of cultural and linguistic taboos are pertinent to cross-cultural studies because they eventually can cause conflicts because of (mis)communications, thus affecting interpersonal relationships with others coming from different ethnic backgrounds. Current events aired on the news and the way they are presented, that is, what the media highlights can trigger positive or negative reactions from the public. In countering racism and bigotry in the media, students need to increase their awareness and be more informed. In-class discussions about racist news reports can only superficially bring the issue to the forefront. To ensure that students comprehend the gravity of the matter and for them to realize the power of words, a collaborative task-based activity was conducted. The students collected news reports and analysed them using textual representations and discursive construction (Bhatia, 1993) of anti-Chinese sentiments that is, scathing pejorative and derogatory labels. A detailed instruction which lists down the tasks (what needs to be done and how) was provided to the students before they were asked to do their online search. It was important to ensure that everyone was on track and knew what they were supposed to do. For a big class, preparing the list of tasks and benchmarks is equivalent to having an in-class logistics strategy, which is paramount to overseeing the effective delivery of the activity.
The students worked in small groups (n=12 groups), collecting news articles (news reports) about anti-Asian racism during Covid-19 (n=110; 119,375 total words). Since the class was held in a computer lab, it was easier for the students to do their online search and collaboratively work on their digital files. They first put their findings on class' Google Forms, which everyone could access and check regularly for any changes made on the document. They had to choose a minimum of five articles (at least one article per student). To minimize sharing the same articles, they were asked to post the URL link of the articles on the class's Google Forms. The news articles they collected were from prominent news agencies such as Aljazeera, BBC News, CNN, Associated Press, Business Insider, Boston Globe, Fox News, New York Times, PBS NewsHour, Philadelphia Inquirer, Taiwan News, The Guardian, Time, and so forth. They had to collect the headlines and list down the anti-Chinese racist language they found. Ample time was given to the students to read the articles, analyse, and discuss. To understand the context of the news articles, they had to note the date of publication, the country in which they were reported, and the URL links. As a group, they had at least five documents containing five different news articles. By compiling everything they collated (the headlines, list of racist language used, and labels used to describe the Chinese or Asians in the news) and putting them on one shared document, it was easier for everyone to see the narrative of the racist discourse that was being recycled by the media at the height of the pandemic. The student feedback report contained the following data: 1) collection of the news articles, listing the headlines and labels they found from those articles, 2) reflections on the kind of narrative being constructed, and the representation of the Chinese and Asians amid the pandemic, 3) reflections on self-perceived conflicts that could arise from the racist issues that flood the media and how they might be affected by these issues, and lastly, 4) their suggestions on resolutions that they feel could help mitigate the problem.

Results and Discussion
The following results and analysis were based on the Taiwanese students' written feedback report where they were asked to construe the characterization of Asianphobia and anti-Chinese views on the news reports they collected. Voyant Tools' Summary gives a general textual overview of the corpus, which includes a detailed summary of the number of words, unique words, longest and shortest documents in the corpus, most frequently used words and notable peaks in frequency across the corpus. The Feedback Corpus has 45 documents with 14,641 total words and 2,019 unique word forms, whereas the three sub-corpora combined has a total of 6,635 total words. The students' collected news articles (n=110) have a total of 119, 375 words. Voyant automatically filters out "useless" but most used words such as the, a, an, in, etc. (Wilbur & Sirotkin, 1992), which the program refers to as stop words. Voyant processes the data and removes them from the list prior to doing the frequency count. The stop words list can be edited if the researcher wishes to add more words to be excluded.

Students' feedback document length
One of the interesting functions of using Voyant is its ability to show which document uploaded is the longest and which one is the shortest (Figure 3). When looking at students' written work, this information would quickly inform the instructor who wrote more; however, this does not suggest that length is equivalent to the quality of one's writing. Nonetheless, it does show who tried. The graph next to the label Document Length also shows where the longest (the dot at its highest peak) and shortest (dot at the bottom) documents appear from the corpora (Figure 3). The graph and individual document labels all have active links, which means the user can click on them and see the actual document from the corpora. This is helpful when clarifying or when doing further investigation.

Frequent words
The frequent words in the Feedback Corpus are (see Figure 4): Asian (177); people (166); Chinese (134); racism (121) and conflicts (118). Unlike other digital text analytical software programs, Voyant does not simply publish a summary of results. Voyant Tools also provides a visualization of the frequency count of each word in the corpus called Cirrus. Cirrus is like Word Clouds where the size of each word indicates its number count (frequency) and importance in the corpus (Heimerl, Lohmann, Lange, & Ertl, 2014). The frequency count of a word suggests the number of times it occurred in the corpus; therefore, the higher the frequency, the larger the text appears on Cirrus.

Figure 4 55-word scale Cirrus output
By looking at the Cirrus output, it is easy to draw word pairings and patterns of phrasing based on how the words were situated or placed together. The list below shows some of these combinations: • Anti + Asian = Anti-Asian • Anti + Asian + racism = Anti-Asian racism • Asian + language = Asian language • Racist + issue = racist issue • Spread + conflict = spread conflict • Asian + virus = Asian virus • Brought + Chinese + discrimination = brought Chinese discrimination The various word combinations above add depth to the lexical power of these phrases, moving from the literal definition to discovering the underlying message behind these terms. These phrases contain pragmatic connotations that carry a lot of emotional weight and meaning. For the students, the overall theme of the news reports they collected focus on Asianphobia or feelings of hate and prejudice against Asians as reflected from the phrases Anti + Asian = Anti-Asian, Anti + Asian + racism = Anti-Asian racism. For the students, the general sentiment is that these news reports spread conflict, thus bringing in more discrimination towards the Chinese (Brought + Chinese + discrimination).

On race and geographical identities
The key words used in the search were Chinese and Covid19, so it was not surprising to see that the term Chinese occurred 134 times. However, what is interesting is that the word Asian appeared 177 times, more than the word count for Chinese. This suggests the strong relationship of these words, not in terms of lexical relativity but more of racial context as being Chinese would right away denote that one is Asian in terms of ethnic background. It becomes an even more compounding issue when one may have physical resemblance from the Chinese of Mainland China yet may have a different nationality for example, Malaysian Chinese, Taiwanese, Singaporean Chinese, Filipino Chinese, etc. Such an association can be problematic as misidentification can happen. People can make assumptions that all Chinese are the same, and all those who look Chinese fall under one ethnic umbrella-Chinese.
The geographical identities for those with Chinese ethnic background in the corpus (n= 71) below follow the Chinese + noun semantic pattern. Using Voyant's Phrases Tool the repeating sequences of words below are organized in order of their frequency of repetition (or the number of times the words were repeated) in each phrase.
• Chinese nationals • Chinese people • Chinese and Asian origin • Chinese or Asian descent • Chinese Canadian National • Chinese Italian • Chinese American(s) • Chinese Australian(s) • Chinese immigrants Another point of interest is that out of the many other nationalities mentioned in the corpus, Americans appeared in the top ten most frequently used words. The term Americans also highly correlates to the lexical terms Chinese, virus, and racism. Further analysis of the news articles suggests that Americans were reported to show racism against the Chinese during Covid times.
The following section provides answers to the first research question: Based on the students' collected news reports covering Covid-19, what are the linguistic evidence(s) of exclusion, discrimination, and racist discourse against the Asians and Chinese?

Labels and patterns of representations
In addition to geographical identities found in the corpus mentioned in the previous section, the students were also able to identify the following patterns of representations in reference to the Chinese/Asians during the pandemic. Looking at the list of labels collected by the students from the news articles, phrases such as Chinese virus, kung flu, chink, Asian virus, yellow peril, etc. were used to refer to Asians ( Figure 5, in order of occurrence). The illocutionary meaning of these representations also suggests that the Chinese were used as scapegoats -someone to blame for the pandemic.

Figure 5 Labels found from the corpus
The students also listed a total of 40 media sources which used the label "yellow peril" as a derogatory representation of the Chinese; yellow is a reference to the colour of their skin, and peril frames them as a danger to society. This supports Wodak's (2001) claims of negative representation wherein the derogatory labels characterize the Chinese as the other, that is, Chinese virus. Whereas the "in-group" in this case are the "the non-Asians-the majority members of the society" These labels further mitigate the Chinese as the source of the virus, hence the culprit to spreading the pandemic. Such framing and repetitive use of such referencing also stigmatizes the Asian community, and further constructs the notion of in-/out-group where Asians make up the out group-the ones excluded, the ones to be avoided. This is also what van Dijk (1998) refers to as a discursive narrative, which is reproduced and reinforced by the society who share the same mentality and belief. By deliberately blaming the Chinese for Covid-19, they (in-group) justify their discriminatory acts against the Asian community. They are doing it for the interest and benefit of the "majority." Figure 6 shows how the term "Chinese" was used in context in relation to the surrounding text (left and right phrases). By looking at the structure of these texts, we can see that all examples from the list of labels collected from news reports were all discriminatory. Applying van Dijk's (1984) 7Ds of Discrimination, we can find an example for each (listed below) based on the excerpts in Figure 6: • Domination-bringing virus to Australia No Chinese are allowed to enter | Australians show domination by not allowing the Chinese to enter the country • Differentiation-We must say no to Chinese | We vs. Chinese • Distance-"No Chinese" signs being put up | Signs that say "stay away from us" • Diffusion-Asians...and coronavirus. These F*****Chinese, they eat everything. Bats, snakes…| transmission of one cultural aspect to another • Diversion-No mask. No Chinese welcome. Chinese people are noisy. | Diverting issues.
• Depersonalization-You Chinese are destroying the world | Destructive characterization of the Chinese • Discrimination-We must say no to Chinese. | Making a distinction or discriminatory judgement against the Chinese

Figure 6 "Chinese" in context
The label, Chinese Virus, has the highest relative frequency of occurrence which appeared from CBS News from the news collected by the students. By looking at the sentence pattern in context, the excerpt below shows an example of how the term was used pejoratively. In this report, it shows President Trump as the instigator, but the full report also warns of the "backlash" that could happen when stigmatization of Asian-Americans continues.
President Trump again used the term "Chinese Virus" to refer to the novel coronavirus on Wednesday, despite calls from global health officials to avoid labels associating the disease with a particular nation or ethnic group. Mr. Trump's Comments have drawn backlash and raised concerns about stigmatizing Asian-Americans.
The excerpt above is another example of media framing (Wodak, 2001;Fairclough, 1995Fairclough, , 2013 where the media frames President Trump by reporting and quoting him of his repetitive use of Chinese Virus. Table 1 shows how the example above can be linguistically dissected using Wodak's (2001) representation strategy. Racist rhetoric perpetuates fear, if not anger towards the Chinese or to anyone who looks Asian as seen from the examples below. The use of expletives and verbal abuse has also been reported by the international media. It is important to note that vulgar and expletive words convey emotions, and they become abusive when there is an intention to harm the people to whom the message is meant for (Andersson & Trudgill, 1990). The following excerpts retrieved from the corpus show two groups of people in the discourse: the victim-the one being shouted at, and the oppressor-the one causing the harm and victimizing others. The victims here are obviously the Chinese or the Asian community as they are the receiving end of the verbal assault. The oppressors are the non-Chinese and those who also feel threatened by the pandemic. Their aggressive attitude towards the Chinese could have emerged out of fear of getting the disease and all the nuances that come from getting sick. Examples of expletives the students found from the media sources are listed below: • F**k Asians … and coronavirus • These f****** Chinese, they eat everything. Bats, snakes, dogs and insects, they should be the only people to die in this epidemic. • You motherfu**ers eat dogs and s**t.
• go back to f****** China • F***ing germ, f*** off The use of threats against someone is even more alarming than a verbal slur. Threats are also verbal but the message behind the threats suggests endangering others (victims of verbal assaults) and the potential risk of physically harming them is imminent (Nockelby, 1994). A threat such as "Kill China virus" does not only express exterminating the disease but the people who were believed to be the carriers of Covid19. Some threats in the excerpts below suggest a command or telling them what to do; they also imply that there is a possible ugly consequence if they do not heed: • we're going to kill you • bringing virus to Australia • No Chinese are allowed to enter the store

Taiwanese students' attitudes and concerns
In answering the second research question, the answers were based on the Conflict sub-corpus (3,846 total words and 898 unique word forms) where the Taiwanese students expressed their attitudes and concerns about the xenophobic discourse circulated in the media. It was found that the students' concerns were born out of the rising tensions between the Chinese community and non-Chinese amid the pandemic; one student even predicted "Asianophobia will rise." The media reports about the verbal and physical abuses that Asians experienced made them contemplate the possibility of encountering the same kind of attack simply because they look Chinese, and the fact that they are ethnically Asian: • People might look down on me.
• People will avoid me and avoid having eye contact as I "look Chinese". • People will joke around and label "virus-spreader in 2020" to me.
• People will exclude me in a community for "looking Chinese". • My deepest concern about this issue is that Asians will be marked with a label like black people. We will face more racism when we travel around the world and be treated unequally because of our physical appearance.
Other reflections are concerned about the economy and how the tourism industry will be greatly impacted. Others have noted the implications toward the already battered cross-strait relationship between Mainland China and Taiwan.
• Asian businesses will be targets of violence.
• Anti-Asian riots could happen again.
• Asian people might not get jobs easily in the racist countries.
• Economic war between America and China.
• Tourism fading (sic) • China and The US will once again have an economic war.
While other students have thought about how Asian students may lose their interest to study/ travel to countries where recent racism attacks took place for safety reasons.
• Asian-Americans will face bullies in their school.
• Asian-international students who study in countries that have anti-Asian racism will have fear (sic) of being treated differently. • Less Asian students want to study abroad (to racist countries).
The excerpt below nicely summarizes the overall reflections toward the devastating ramifications of Covid19 on students' lives and life in general, but this student mentioned a very important issue which is somehow buried under the economic slug, that is, mental stress that everyone (in)directly affected by the pandemic must be suffering: I am mainly concerned about the world economy and the mental condition of people in crisis countries. It appears that many SMEs or restaurants have already gone bankrupt and unemployed people are increasing (sic) with coronavirus. I think the world economy could break down at any time. In addition, there are no medicines that can stop Coronavirus now and no one knows when we will get back to normal life, just wait to increase the number of patients who contract (sic) the coronavirus every day. People easily feel (sic) stress and mental conditions will get worse.

Students' suggested resolutions
The Collocates Graph (in Voyant Tools) shows keywords and terms (tokens) that appear in proximity and are presented in a network of graphs (Figure 7). The keywords are shown in blue and are linked to collocates (in orange). The user can hover a word to see its frequency in the corpus. The words can also be dragged around the canvas to see possible collocation patterns. In Figure 7 below, three keywords are shown: Asian, Chinese, and people, and the light grey lines indicate the connection of these terms. As it appears, the word Chinese (as keyword) has strong collocations with the terms people, virus, and Asian, that is, Chinese people, Chinese virus, and Asian Chinese. Whereas the word Asian has more collocational connections with community, racism, involvement, and resolutions (as seen from the grey line connecting the terms, see Figure 7), that is, Asian community, Asian racism, Asian involvement, and Asian resolutions. However, the word Resolutions (as keyword) shows strong pairings with the terms Asians, community, and offer. Whereas the word Community (as keyword) was highly juxtaposed with only one term: resolutions; this collocational relation carries a more profound message-it reflects the students' understanding that this conflict requires a collaborative effort of/from the Asian community for it to be resolved. This was an interesting finding because it puts the whole issue into perspective. The increase of Covid-related racism and violence is a sad reality of the world we live in now. However, what is happening cannot be disassociated from the Asian community. Whether they are East Asians or Southeast Asians, they are still Asians. Like what was mentioned previously (see Defining Asians), the pandemic triggers an outburst of racism and hate towards Asians. These results from the corpus show that the students understand the importance of unity and community, that is, the Asian community needs to stand in solidarity against Asianphobia. Despite Asians having ethnic and cultural differences, they must find a common ground. Their stories and experiences of discrimination can be a unifying force to stay aligned with their ethnic identity-being Asian.

Figure 7 Collocates graph for top keywords
The students' reflections on the kinds of resolutions they could offer regarding the issues brought upon by Covid19 provide an interesting view of how they perceive not only their role as students but their cultural identity as Taiwanese, and what Taiwan as a nation could do to help other countries. The self-created corpus for Resolutions ( Figure 8) have a total of 2,306 total words and 688 unique word forms, with the following most frequent words in the corpus: people (31); Taiwan (16); help (15); think (15); racism (13); The frequency of their occurrence in the corpus are visible in the Cirrus output ( Figure 6), that is, the larger the size of the word, the higher the frequency. What is interesting is how the words are placed side by side thus helping readers to create phrases which make sense. For example, the phrase "people minimize racism" is a message that is strongly being echoed by the students who believe that racism should be minimized if not totally eradicated. A few more interesting phrases that can be seen from the cirrus are students try to tell + Taiwan mask + help + example + countries. Putting them all together suggests that students think Taiwan could help other countries either by giving Taiwan-made masks and by setting a good example in combating the pandemic. With the slogan "Taiwan can help, and Taiwan is helping", the Ministry of Foreign Affairs (MOFA) announced the "offer of a total of 10 million masks to Taiwan's 15 diplomatic allies, 11 European countries, and the United States..." (Taiwan to donate 6 million masks, 2020, par. 2). Kyle Vass of The World, a public radio program, reports that "Taiwan leads the world as the most-prepared and best-equipped nation to fight the pandemic" (2020, par. 2) and therefore can set a good example for other countries to follow. However, The World also states in the same report that the country's efforts are being "overshadowed by global politics" (par. 1).

Figure 9 "Taiwan" in context
Looking at lexical items in context, the Context Tool shows the phrase or sentence in which a keyword occurred, thus giving the readers a better understanding of how that word was used in its appropriate context. For example, in Figure 9, the word Taiwan is the keyword that is being observed, and the output shows its position in the sentence structure it appeared in, thus giving an expanded background of the text. The first sentence in the example below states the need to "also tell the world that Taiwan can help." The second example also suggests telling the world that "Taiwan which comes from Asia can help." However, other than what has already been pointed out about Taiwan's ability to offer medical assistance, the collocations output also highlights the students perceived self-identification in reference to being a citizen of Taiwan, and Taiwan as a sovereign democratic country: Identifying themselves as Taiwanese seems to signify a sense of national pride and reinforces their cultural identity; the examples from the collocation output below suggests the importance of being identified as Taiwanese with a Taiwan passport: • Maybe we can show them our passports to prove that we are Taiwanese when we travel in a foreign country • A backpack which has the sign "I am Taiwanese."

Figure 10 "Asian" in context
As mentioned in the previous section, the students recognize the importance of standing in solidarity with the Asian community. This can be done by getting involved and offering help, that is, Taiwan can help. Looking at Figure 10, the surrounding contexts of the word "Asian" are strikingly thought-provoking. The students understand that Taiwan is in Asia, and thus, they see themselves as Asians: • As Asian [sic], we could help by donating… • As an Asian student, the most I can… • As a student and an Asian, making a commitment to speak… However, they were careful not to categorize themselves as Chinese as that would cast a shadow of doubt where their patriotic loyalty lies. Based on the entries that they have written on the feedback, they were acceding to being Asian, but only as Taiwanese Asian. They expressed their willingness to support the Asian community, and to stand with them in solidarity to fight against Asian hate and anti-Chinese rhetoric on the News. The students were mindful of their language when drawing distinctions between Taiwan and China; there were no racist-remarks against the Chinese. They expressed their empathy and sympathy because they could relate themselves to what was happening to other Asians and Chinese in various parts of the world. What was clear from their written feedback was their understanding of the fact that Covid-19 was the latest reiteration of racism. And although students represented themselves as Taiwanese, their physical resemblance to the Chinese is just too close. They are ethnically related, historically, and geographically connected. And like the vignette mentioned in the introduction about the assault towards the Taiwanese student in New York, it was apparent that they too could potentially be a victim of hate crimes. At the end of the day, it really does not matter whether they are Taiwanese or Chinese, Asianphobia affects all Asians-they carry their race on their face.

Conclusion
In this paper I have analysed some of the issues surrounding racism in the news about Covid-19 and Taiwanese students' sentiments using corpus-assisted critical discourse analysis (CDA). The CDA performed in this study was a subjective interpretation of the text, however it offered thought-provoking insights about the contextual clues seen from the linguistic patterns. The xenophobic expressions, verbal and physical abuses toward Asian Chinese were very alarming. The students' written accounts of fear revealed how they were impacted. Nonetheless, it has also heightened their passion to express their cultural and ethnic identity as Taiwanese. Politically sensitive as it is, these students are aware of the ramifications of racism and the deadly power of words. They also recognize that the pandemic has also brought Taiwan into the limelight with the slogan "Taiwan can help," which the students were also loudly echoing. The findings also suggest that the resolution to the conflict can come from the Asian community itself. They need to get involved and stand in solidarity against Asian hate. However, with the current upheaval of being mistaken as Chinese from mainland China, they felt that a clear demarcation line between the two nations and a stronger cultural identification for Taiwan was crucial. This study revealed the students' perceptions of their racial identity, that is, what it means to be Taiwanese and what it means to be Asian. How strongly they can hold on to that racial representation amid the strained Taiwan-China divide is a matter of contention-one that is worthy of future investigation. Nonetheless, one of the limitations of this study was the small size of the corpus. For further research into this field, a bigger corpus data and longitudinal approach would be helpful for a wider coverage of racism during Covid times.