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Public perception of COVID-19’s global health crisis on Twitter until 14 weeks after the outbreak

Abstract Because language represents advanced aspects of human cognition, studying linguistic styles and figurative meaning have proven effective in measuring embodied cognition about the external world. This article defines the most worrisome topics people discussed from Weeks 1 to 14 after the outbreak and compares the message delivered by the literal use of words to the figurative use of metaphoric expressions. We bootstrapped representative data from Twitter over 14 weeks since the inception of the outbreak to be analyzed thematically using the Linguistic Inquiry and Word Count (LIWC) 2015 software as well as corpus tools. The MetaNet database, corpus tools, and manual annotation were used to detect expressions that can be linguistically mapped to the formalized list of conceptual metaphors. The most frequently tagged themes included ‘the outbreak of the pandemic, its epidemiology, its prophylaxis measures, national and world economies, media’, as well as the ‘signs and symptoms of COVID-19’. Although LIWC-based analysis showed English-speaking tweeters maintained high levels of analytical thinking, elevated levels of anger, anxiety, and doubtfulness, there were discrepancies and improper conceptualization of the clinical picture of the pandemic.


Introduction
Since the pandemic of the severe acute respiratory syndrome (SARS) at the inception of this millennium, the definition of the leap year has been redefined to connote a new epidemic. The coronavirus outbreak COVID-19 concluded an episode of SARS-like pandemics, including 2009's swine flu, 2013's bird flu, 2014's Ebola, and 2016's Zika. This regular outbreak of health crises has made ordinary citizens feel threatened and manipulated. Previous studies on Ebola-a flu-like syndrome that can progress to full hemorrhagic fever with lethal multiorgan failure-Zika, and 2009's H1N1 promised strategic plans and lessons learned from each outbreak in terms of low-cost diagnostics and effective management, using synthetic biology, in pandemic regions to better confront new global health crises (Thompson et al., 2017). This article explores the themes that people are most concerned about COVID-19 after its outbreak using a semi-supervised approach. We also compare the conceptualization of the pandemic with the figurative use of tweeters with reference to MetaNet's formalized list of conceptual metaphors. 1 How people communicate about this pandemic on Twitter is also analyzed using Linguistic Inquiry and Word Count (LIWC) tool.
The remainder of this article is organized into six sections. Section 2 reviews related work that motivated this study and assigned similar methods to measure the public attitude toward the past attacks of viral pandemics. Then, the theoretical background underlying the corpus-based psycholinguistic analysis is delineated in Section 3. The proposed method, data collection, and procedure of analysis are explained in Section 4. Section 5 presents the thematic findings that tweeters focused on and link these themes to the LIWC-based psychological metrics. Section 6 discusses the introduced results in view of cognitive, social psychology, and grounded cognition. The final section draws conclusions and offers recommendations for further research.

Related Work
The user-generated content on social media streams reveals much about their mental states when they attempt to deceive or to express panic. Duran et al. (2010) used Coh-Metrix, unique in that it tracks linguistic features based on cognitive and social factors, to evaluate deceptive and truthful conversations that occur within a context of computer-mediated communication.
To investigate the efficacy of Twitter as a tool for measuring perceptions of the public in global emergencies, Chew and Eysenbach (2010) developed Infovigil, a public health surveillance system to collect data from Twitter and extract textual information from the corpus of the compiled tweets. They focused on tweets with keywords that refer to the 2009 H1N1 virus. They concluded that out of fourteen different types of information, tweets that provided resources, particularly news websites, were the most prevalent, with personal experiences coming second. Using the World Health Organization (WHO)-based nomenclature of H1N1 supersede the popularly used term 'swine flu' during the outbreak of the pandemic.
Distress, worry, and fear responses to the 2014 Ebola pandemic were studied during and after the resolution of the global crisis to analyze the integrity and continuity of quality care (Heymann et al., 2015;Thompson et al., 2017). They concluded that the standards of hygiene in communities and health facilities should be raised. Interdepartmental collaboration across all sectors and community stakeholders is required to strengthen capacity for infection control, occupational health and safety, and leadership coordination. Fu et al. (2016) examined how people on Twitter reacted to the surge of the Zika virus in 2015 and 2016. Their primary concern was to provide baseline data for Twitter health communication about the virus. After collecting more than 1 billion tweets during the outbreak of the virus, they captured all tweets that contained the keyword 'Zika' and analyzed tweet content, date posted, and the language of the tweet. Furthermore, they employed topic modeling to identify the dominant themes in the corpus of tweets and generate major themes. The societal impact of the outbreak, reactions of governmental and private sectors to the outbreak, the negative influence of the virus on pregnant women and babies, transmission routes, and case reports dominated Zika-related tweets. However, the thematic focus seems to have diversified much with COVID-19.
In another Zika-focused study, Stefanidis et al. (2017) examined the prominence of linguistic and metalinguistic features in interpreting the behavior of people during a public health emergency. For that purpose, they collected more than 6 million tweets from December 2015 to March 2016. Their primary focus was on the geographical distributions of these tweets, the types of users, and the concepts that sprouted as a result of the outbreak. Using spatiotemporal analysis, they discovered that the concern of Twitter users toward the virus extended from local contexts in South America, the origin, to North America, and then expanded worldwide. Global organizations, such as WHO and Centers for Disease Control and Prevention (CDC), were the most dominant accounts during the outbreak.
Trusting these organizations does not seem to be the case in 2020.
Because risk communication channels, whether environmental risk communication, disaster management, health promotion and communication, or media and communication, are all viewed on an equal footing, Guidry et al. (2017) explored how health organizations communicate with the public on Twitter and Instagram during the health crisis. They examined Ebola-related social media posts by three major health organizations, CDC, WHO, and Médecins Sans Frontières on Twitter and Instagram, focusing on the content and context of the communication that was used during the outbreak and the responses they elicited from the public. They concluded that solution-based messaging, incorporation of visual imagery, and acknowledgment of public fears and concerns could prove effective if hosted on social media platforms.
Given that the COVID-19 pandemic has dominated the social media streams, this article studies representative samples of user-generated content that was tweeted from 24 January 2020 to 30 April 2020, using LIWC, MetaNet, and corpus tools, to explore what psycholinguistic analysis of these data reveals about the raised Coronavirus-based folkloric concerns and their justifications.

Theoretical Background
Following Linell and Manstead, who introduced their psycholinguistic theory of communication accommodation in 1991, Danescu-Niculescu-Mizil et al. (2011 proposed their LIWC-based probabilistic framework toward analyzing the context of Twitter conversations as a large-scale, real-world platform.
From a psychometric perspective, even though function words such as pronouns, conjunctions, and prepositions connote very little content-related meaning, they constitute 55% of word use.
In addition to their apparent ubiquity, psychologically speaking, style words can be indicative of a writer's personality and psychological traits. For instance, to write a coherent narrative, a writer relies on conjunctions, while insight and causal words are more likely to reflect an active process of reappraisal. Drawing on these insights, LIWC was developed to examine texts with the intention of unmasking the psychological signature of a writer (Pennebaker, 1997;Pennebaker et al., 2001;Tausczik and Pennebaker, 2010).
Based on the social categorizations of individuals, LIWC organizes people's attitudes into four psychological categories. The first of these is 'Analytic', which comprises the different forms of analytical thinking. This category is a factor-analytically derived dimension that is based on eight function word dimensions. Words that belong to this category include cause words (reason, because, effect), exclusives (without, but, except), negations (not, never, none), insight words (meaning, realize, know), quantifiers (many, some, greater), tentative words (maybe, perhaps), and certainty words (absolutely, always). The dimension measures the degree to which individuals use words that indicate logical, formal, and hierarchal patterns of thinking. That is to say, people with low analytical thinking capacities are more likely to write and think using narrative language focusing on the here-andnow, and personal experiences. The second category, Clout highlights the relative social status that reflects the confidence or leadership that people exhibit, whether written or verbally communicated. LIWC's algorithm was designed based on results obtained from a series of studies where people were interacting with one another. 'Authenticity', the third category, displays people's perceptions of themselves as genuine or truthful. Using a more personal, humble, and vulnerable style was found to be suggestive of authenticity and honesty. Finally, 'Tone' evaluates the emotional inclination of a writer. LIWC's algorithm for tone is designed so that the higher the value, the more positive the tone. Numbers below fifty imply a negative emotional cadence. The reference values for each measure LIWC-2015 calculates are provided in the published manual (Cohn et al., 2004 ;Pennebaker et al., 2015).
Based on Barsalou's review of grounded cognition and social cognition (2008,2010), theories of cognitive linguistics and cognitive psychology must implement simulation mechanisms, in principle, to meet the core cognitive functions, including type-token binding, inference, productivity, recursion, and propositions. Thematic roles of verbs are bound to values (e.g. binding the instrument role for 'arranges hair' to comb). Content words combine to form novel phrasal and sentential structures. Phrasal structures embed recursively. Propositions extracted from linguistic utterances represent meaning beyond surface structure. Therefore, LIWC and frame semantics, and cognitive grammar are the best frameworks for extrapolating from large-scale corpora based on cognitive grounds.
Conceptual Metaphor Theory (CMT) proposed that the most central metaphors are grounded in bodily experience. In CMT, metaphor components include a Source domain, Target domain, experiential basis, neural structures corresponding to the domains, relationships between the domains, metaphorical linguistic expressions, mappings, entailments, blends, nonlinguistic realizations, and cultural models. (Lakoff and Johnson, 2008). MetaNet, a structured repository for a formalized list of 684 conceptual metaphors, has integrated CMT and 'frame semantics' in a single language resource to facilitate larger scale metaphor-based analysis by mapping the Source domain (based on shared embodied experiences) to the Target domain (more conceptually abstract and viewpoint-dependent). Both the Source and Target domains are represented as frames, which are schematic representations of different kinds of experiences, objects, and events. MetaNet structures metaphors in a network with hierarchical, structuredefining, and non-hierarchical relations similar to the network governed by relations between frames (Stickles et al., 2016). This study merges the corpusbased semi-supervised approach of analyzing and categorizing the collected tweets with the findings retrieved from LIWC-2015 and MetaNet. Detecting similar themes is followed by a cognitive linguistic analysis of the concordances in view of the psycholinguistic theory of communication accommodation and grounded cognition.

Methodology
In order to collate tweets mentioning COVID-19, we used the 'Get old Tweets' tool. 2 The tool was ushered to crawl publicly available tweets containing the keywords 'Corona', 'Coronavirus', and 'COVID-19'; and synonymous term. In addition to the content of these tweets, the tool also provided metadata such as tweet time and the number of replies, retweets, and favorites. In order to have representative samples of data, we collected approximately 10,000 tweets for each day, starting from 22 January 2020 to 30 April 2020. The final number of tweets added up to 992,593 (12,244,567 tokens). The collected data can be accessed here: Github link: https://github.com/ Muhsabrys/Coronatweet. 1 We contrasted our corpus against the English Web corpus 2015 (enTenTen15) to extract unique keywords and n-grams in order to identify characteristic themes in the compiled corpus. Two English linguists, who were blinded about the aim of the study, annotated the top 1,000 keywords and chunks manually according to their respective semantic fields. Their annotation generated the following classes: conspiracy, economy, media, mortality, origin, outbreak and epidemics, politics, prophylaxis, threat, signs and symptoms, and treatment, among others. The difference between their categorizations was not statistically significant.
After analyzing the data using corpus tools, we used the LIWC-2015 software program (Pennebaker Conglomerates Inc., Austin, Texas, USA) to retrieve the lexicon-based thematic categorizations for the collected tweets and link the generated categories to the overall psychological attitude of the users based on LIWC's algorithm. LIWC contrasts the analyzed text to 120 categorizing lexica, which are experimentally validated, to implicate on the psychological content of the original writers. Sketch Engine V R and AntConc V R were used to generate the concordance of keywords with bilateral windows of five words for each node. The annotation of themes was manually performed. ANOVA test uses the sums of squares of the deviation of the observations from respective means. The tradition of arraying sums of squares and resulting F-statistics in an ANOVA involves the separation of the total observed variation in the data into individual components attributable to various factors as well as those caused by random or chance fluctuation. SPSS 25.0 software for Windows (IBM Inc., Chicago, IL, USA) was used to generate the F-values for a oneway ANOVA test and posthoc Tukey HSD, as well as the corresponding statistical level of significance. A p <0.05 was considered significant.
Not only has COVID-19 perpetuated fear, political and economic turmoil, global health concerns, and assent worldwide, but frustration and lack of confidence in the flat digital world were traced among average citizens as well. Panic over unproven treatment for COVID-19 causes fears over the lack of a cure, antiviral drugs, vaccine to treat, and the unknown consequences of such a morbid global health crisis. Inferring from the Internet Protocol addresses and country of origin, the tweeters were mainly based in the USA, UK, Canada, Australia, Zimbabwe, South Africa, and Hong Kong.

The most debated themes up to
Week 14 'Outbreak and epidemics' was the most dominant over the past 14 weeks. People were particularly concerned about the increasing number of infected cases at the global level. The theme also covered places where the virus is currently spreading. Interestingly, even though the situation in China was contained faster than in Italy, people still talked about China ten times more frequently than they mentioned Italy and Italian patients. People were also attentive to the ways the virus spread via human to human transmission, responses of official bodies, and the gravity of the virus. Finally, comparisons between the coronavirus and other diseases such as SARS, Bocavirus, Parvovirus, and pneumonia were also present in this theme.
The second observed theme in the corpus of tweets was prophylaxis; this includes the set of medical and public health precautions taken to prevent the virus from spreading. Handwashing, carrying hand sanitizers, and wearing face masks were the most frequent means of self-protection. As for the precautionary measurements taken by governments to contain, and hopefully to prevent the spread of the virus, herd immunity surpassed all other safety measurements. The imposed quarantine was another subject of discussion, especially the penalties that await those who violate its conditions. The theme also covered advice provided by institutional bodies such as WHO and CDC, social distancing as a means of prevention, and finally, hospital-related equipment such as protective gear and testing kits.
The negative impact coronavirus has had on world economies made tweeters anxious about the present and future situation of their national economies and, subsequently, their personal financial conditions. The basic concerns were directed toward the stock market; in their conversations about the stock, they used words such as 'plummet', 'drop', 'damage', 'crash', and 'lose' to describe the status quo. They are of the belief that an 'imminent recession' is unavoidable. Countries that received the highest levels of concern were, respectively, China, the USA, Canada, and Australia. The second economic system that made tweeters apprehensive of its status was supply chains, with food and logistics being the most talked about items. Figure 1 shows the worldwide spread of coronavirus until the 14th week after the outbreak. The countries that are highlighted in darker colors reported the greatest number of confirmed cases. The correlation between the list of countries generated from the studied tweets and the charted ones is strongly positive (p < 0.05).
Since the onset of the outbreak, the different media platforms have played a crucial role in raising public awareness, sharing the latest updates, and providing virus-related information. News covering press conferences, official declarations and responses, and sources of information was the most common. Furthermore, responses to the worldwide campaign and Twitter hashtag, 'stay home,' were also among the highly frequent aspects associated with news. Twitter users were also attentive to 'fake news', 'disinformation', and 'hoaxes' that were shared by several 'websites'.
Stories about the first infected cases and the Chinese city of 'Wuhan', where the spread of the virus began, received serious consideration from Tweeters. Other areas that were mentioned in this theme are Iran, South Korea, and Japan. As for individuals, the Chinese ophthalmologist 'Li Wenliang' was known for being the first Chinese physician to warn his colleagues of a potential outbreak of a SARS-like virus. Wenlaing later died after being infected by the virus.
All individuals and entities involved in the process of treating patients were positively represented in our data. The sixth theme, 'treatment', refers to the steps taken by hospitals, health-care systems, and physicians to treat people who were infected with COVID-19. Doctors and medical staff members were lauded for their attempts to raise people's awareness as regards self-protection. People also talked about drugs that are being used as vaccines to fight the virus; these include antimalarial and antiviral drugs. Many individuals reported their experience of recovering from mild corona infection with domestic treatment and bed rest. Nonetheless, little was mentioned about the successful treatment of infected cases with antiviral drugs (e.g. remdesivir), antimalarial drugs (e.g. chloroquine and hydroxychloroquine), HIV/AIDS-treating drugs (ritonavir/lopinavir), interferon-beta, and new lines of treatment. Figure 2 shows concordance lines for  Political figures, governments, healthcare systems, and health officials were the major aspects that the theme of politics encompasses. As for political figures, several (British, Canadian, Japanese) prime ministers were mentioned, and the American president and his vice president were also referred to or quoted. As for governments, tweeters paid due attention to 'the Chinese, British, Italian, Australian, and local' governments. Verbs that were used with 'government' as a subject included 'handle', 'announce', 'stop', and 'try', while 'urge' and 'construct' were the two main verbs that were used with 'government' as an object. Healthcare systems were 'overwhelmed', 'crashed', and need 'improvement', according to tweeters.
Many linguistic clues highlighted the tendency of tweeters to adopt conspiracist ideation. A few thousand individuals worldwide considered 'the whole Corona thing', a scam or a plot directed to sabotage world economies. China, also referred to as 'Communist China', was described as having a 'ruthless' and 'oppressive' regime, which 'caused' the spread of the virus. They also referred to China as the 'black swan', plaguing the world with humanmade lethal contagion. Nonetheless, this conspiratorial attitude became less remarkable by the end of the ninth week after the outbreak. Moreover, other tweets focused on debunking these theories and assuring that the virus is natural. Finally, the virus was also seen as a form of 'propaganda' whose purpose is nothing but 'fear-mongering'.
Coronavirus was also recognized as a threat that could aggravate and have even more serious repercussions. People compared the virus to climate change, and other global crises , while others criticized people's concern over coronavirus while they showed no reactions against violations of the environment and downplaying its gravity. Another recorded concept here, which overlaps with the previous theme of conspiracy, is the belief that the world is being prepared for forthcoming biological warfare and/or that the virus itself is a biological weapon being used by some countries against others.
Comparing coronavirus and 'common flu' or 'common cold' was carried out across the first 9week interval. Furthermore, tweets were full of informative signposts that were apprising people of the signs and symptoms of the novel coronavirus. Particularly speaking, the virus was described as an acute respiratory illness that is like SARS. Of all the symptoms that physicians and health officials referred to, 'sore throat' was the single symptom that tweeters considered. The tweets collected over the next 5 weeks demonstrated a better recognition of the pandemic virus.
Strategies that were adopted by governments based on recommendations from health organizations were also present in the tweets data. Particularly, people discussed two aspects of the travel ban (which was later extended) and social distancing. On a relevant note, people also referred to the efforts exerted by scientists and laboratories in favor of developing a vaccine against the virus. Most tweets that addressed this issue focused on news of vaccine trials. Finally, issues that did not fall under any of the previous themes were annotated under 'others'. This theme included discussions from buying, drinking, or boycotting 'Corona beer' to sharing ideas that could be implemented to contain the outbreak.

The frequently used conceptual metaphors until week 14
A linguistic metaphor instantiates a conceptual metaphor linguistically, to be distinguished from visual metaphors. Lexemes evoking theft, such as in stealing time, robbed me of my health, and poached his idea, may entail that time, health, and ideas are valuable things that can be 'taken'. MetaNet structures linguistic metaphors in a network with hierarchical, structure-defining, and non-hierarchical relations similar to the network governed by relations between frames. For the metaphoric use, the top conceptual metaphors Englishspeaking tweeters evoked are 'disease treatment is war', 'government is a leech', 'corruption effect is disease effect', 'causing economic hardship is causing physical harm', 'ineffective government is a poorly-managed business', 'nature is a strict father', 'opportunities are objects', and 'government is a shepherd'. Table 1 provides examples of the linguistic realization of each conceptual metaphor.
Coronavirus has waged war against the worldwide consolidated forces and is seemingly defeating them.
'Disease treatment is war' was the most frequently used conceptual metaphor to which many child metaphors belong. Doctors and all medics were considered frontline soldiers (e.g. 'A medical officer is a soldier. Not your common commando but a soldier fighting against terrorists like the current coronavirus'). Masks, gloves, sanitizers, and the other necessary equipment for infection control were considered the ammunition needed to win this war. Staying home was considered a military post where the least actively fighting troops should commit themselves too. The battlefield exceeded the hospitals and medical facilities, as hot spots, to include the entire globe. Nevertheless, this unidirectional war was not the only battlefield the folks recognized. 'Argument is a war' is another conceptual metaphor that was detected in many tweets. Some governments launched against religious disparities based on politicizing the global health crisis. Coronavirus was notoriously propagated to launch a war between local theo-political enemies who normally portrayed severe quarrels and false allegations against each other. The use of coronavirus panic in this context demonstrated how destructive and lethal weaponizing words would be if misused. For example, the Indian government alleged that Muslims were vectors of the pandemic that ought to be eradicated.
For the first 6 weeks of the outbreak, the coronavirus was conceived as deadly and lethal to stop China. Because China represents a source of concern that needs extraordinary weapons other than missiles or any other traditional armamentarium, COVID-19, which hit China first, was considered the weapon of choice. Examples for the relevant tweets include 'Corona Virus looks more like a biological weapon being used to deal with China', 'Covid19 is a # bioweapon', and 'Coronavirus Makes America Seem Like a Civilization in Decline'.
Other conventional metaphors were also detected. They were portraying the virus as a rigid and stubborn human being that has the strength to penetrate regardless of restriction procedures of wearing protective equipment and is stubborn enough not to show any sympathy to young or geriatric populations. The lethal virus did not even exclude heroes serving communities and sick people and made the least vulnerable the most vulnerable. As a wicked person, this virus, as people tweeted, has no limits to the repercussions it may cause. People should be open to any unprecedented consequences and expectations.
Yet, COVID-19, as a skillful diagnostician, showed the chronic inner conflict between divergent and competitive sectors in each country too. Coronavirus evinced how pathetic Britain was when it went begging to Europe for help as well. The paradoxical depiction of COVID-19 as a good tyrant was also observed. Figure 3 shows how people evoke this triumphant to wipe out the psychologically sick and societally obnoxious individuals.

LIWC-based analysis of the collected tweets
As Table 2 shows, the levels of Analytical thinking, compared to the reference values, have been roughly stable and high over time. This suggests that, during the defined 14-week interval, tweeters were maintaining logical, hierarchical patterns of thought, and cognitive complexity in their tweets, rather than the narrative style (Appendix 1). Moreover, the high score in Clout represents a high level of confidence; this confidence declined over the weeks but was highly increased at the end of the last 2 weeks. As for authenticity, the low range of scores is a sign that tweeters want to distance themselves from their readers. Finally, the low-and mid-range scores of 'tone' indicate fluctuations of feelings over the 14 weeks. The tone was suggestive of anxiety and hostility during most of the weeks until a change was observed over the last 2 weeks of March 2020, when ambivalent feelings began to arise. The statistical difference in expressing Analytic thinking, Clout, Tone, and Authenticity, over the studied 14 weeks, was significant (p <0.05).  In the case of COVID-19, although fear and panic were mitigated by positive energy and focusing on pandemic mitigation, frustration and assent were escalating weekly because the global organization and research facilities played no effective role in finding treatment modalities if treatment options were offered at all. The statistical difference in expressing negative emotions, fear, sadness, and anxiety was significant (p <0.05) ( Table 3).
Psychologically speaking, deception requires more cognitive effort and high processing abilities than when telling the truth. Results show that tweeters were both honest and individualistic. This is because high scores for insight-expressing words indicate high levels of individualism and rationalization. This is congruent with the mean scores for both Cause and Discrepancy, for, apparently, tweeters were trying to rationalize specific appealing ideas and denying other less appealing ones (Table 4) .
Many tweets implied conspiracist ideation about fictionalizing reality on the pandemic in the first 5 weeks. People accused secret hegemonic forces of acting immorally against China. With the strict lockdowns, few tweets accused global governments of evil military movements in the dark. However, the most frequently prompted accusations were leveled at the WHO and CDC.
Individual attempts at domestic treatment, which were mostly based on folkloric and alternative or herbal medicine, were reported to be the only working solution, given the lack of governmental therapeutic and intervention plans. The applied governmental retract-and-wait  Digital Scholarship in the Humanities, 10 of 16 strategies aimed to restrict population movements, prohibit assembly, and delay schooling and noncritical work. Although death-related values might be attributed to the passing news about the mortality rate, reference to religion gradually increased while leisure talk and money-speak decreased. A shift to discussing metaphysical issues was also observed (Table 5).
People, although clinging to scientific and medical knowledge, never ignore divine help. No matter how luxurious life is, people during hardship return to their God and seek help . Examples for this category of tweets include 'pray with faith that God may destroy COVID-19 and GOD Will destroy COVID-19', 'this plague ends like in Egypt in the days of Moses people are getting sick some are dying right away', 'Virus Experiment: A thoughtful look @ how God may want to use this virus for His glory and our good' and 'Millions Are Turning to God'. Overall, Fig. 4 illustrates how English-speaking tweeters interact with the weekly rise of confirmed cases.

Discussion
Although global health crises have focused, over the past few decades, on the emergence of antibiotic resistance, hepatitis C virus, HIV, the incidence of obesity, and associated metabolic syndrome diseases with many effective solutions offered (Njoroge et al., 2008;Fig. 4 Public interaction with the weekly rise of the confirmed cases Hamblin, 2016), the focus has shifted to viral pandemics and establishing core public health facilities and social care systems, improving emergency services, educating civil society, communications, and media, and changing individual attitudinal behavior (Jacobs, 2007;Pardee et al. 2006;Schmälzle et al. 2013). The external cognitive and affective knowledge systems, which are usually based on cognitive language resources, operationalize their tools on corpora that are compiled from Twitter. Users of Twitter, as a bloglike social network site, are constantly applying linguistically unique styles to best communicate their messages within the 140-character limit. However, collecting a representative corpus, with adequate effect size, is inversely proportional to the duration of the event the tweets cover and the intensity of social engagement with a specific event. The greater the number of people who are inclined to interreact positively with a topic over a long period, the less likely collecting a representative corpus from Twitter is to be attainable. That makes studying a 12-million-token corpus covering approximately the entire time range since the outbreak of coronavirus highly authentic. Although all tweets were bootstrapped from non-organizational accounts, the retweeting of BBC News and Fox News could not be discarded. For these retweets, it seems that media streams hadshaped,toagreatextent,theconceptualizationofthe global threat. However, studying this influence does not lie within the scope of the present study.
We used corpus tools and LIWC-2015 to analyze the compiled corpus. Corpus linguistics has many applications in psycholinguistics (e.g. syntactic priming/persistence), stylistics, sociolinguistics, forensic linguistics (e.g. authorship attribution), culturomics, and gender studies among other branches of English and non-English interdisciplinary studies (Boot et al. 2017). There is a growing number of corpus linguists who use object-oriented programming languages to do their data processing. Given that evaluating global concerns was our main objective, we did not aim to study the cognitive-linguistic style of each tweeter. Essam et al. (2019) used the principles of frame semantics, conspiracism, and corpus-based linguistic cueing to explore the common folkloric geopolitical concerns of Egyptian tweeters about the changing cartography of Egypt. They integrated FrameNet, LIWC, and corpus tools to analyze the compiled data to conclude the core elements of the proposed deception scenario. Ansumana et al. (2017) explored media hype during the outbreak of the Ebola virus to analyze the misconstrued conspiracy theories that were propagated by the local media and community gossip about the perceived origin of this pandemic. O'dea et al. (2017) used LIWC to analyze suicide-related Twitter posts. Community fear, stigma, apprehension, and misunderstanding of the role of healthcare centers operating under prevailing epidemic conditions were the predisposing factors for engaging in conspiracist ideation. Similarly, finding a scapegoat to blame for the erupting disaster is consistent with regulating the global panic so that people might feel that there will be a way out once the predator is known. We also applied CMT to detect the tweeters' perception of the external world. Evidence from neurolinguistics supports that the left anterior inferior parietal lobe and the left amygdala are activated more by metaphorical sentences than by literal ones. The CMT-based analysis showed that the recognition of COVID-19 is controversial. Some tweeters considered it destructive while others regarded it as corrective, reformative, or punitive.
Similar to previous reports on facing epidemics, the ill-fitting prophylactic measure was confined to encourage global populations to wear facemasks and disposable rubber gloves all day long even though they are ineffective measures for controlling the infection. People were also discouraged from practicing aggregation-based rituals and discretionary travel. Medical facilities quarantined infected individuals to a certain extent. All consolidated effort and published research papers during past global health crises and the COVID-19 pandemic failed to introduce one fast, effective, therapeutic hope. This failure heightened the impression that all that the digital world learned about medicine and fighting health crises was no more than pseudoscience. This unpreparedness propagated panic among the entire globe until the cases began to decline in the 14th week.
Analogically speaking, coronavirus shocked the world. Even great countries did not escape the outbreak. In fact, as a powerful virus, it attacked the developed countries first. Countries in search of a treatment for this virus had to take actions and restrictions that made them consolidate efforts with other friendly and nonfriendly countries. Over time, especially near the 12th and 13th weeks after the outbreak, this quest for treatment made all countries sacrifice the welfare of people and apply strict measures to passively resist the unknown. The mitigation procedures implemented are also accepted in the hope of minimizing the transmission of the morbid infection.

Conclusion
COVID-19 perpetuated fear, political and economic turmoil, global concerns, and assent worldwide. This article provided insights into revealing the major topics that people were mostly concerned about regarding the emerging global health crisis. We were particularly interested in studying the psychological and social characteristics that were trending in the analyzed data. Out of fourteen different themes that preoccupied tweeters' attention, they were particularly focused on the outbreak of the virus, ways to fight and contain its rate of spread, and the economic repercussions resulting from the outbreak. Furthermore, countries that received a notable level of interest from tweeters were China, the USA, Japan, and Italy. Using LIWC, we were able to determine the dominant psychological dimensions transmitted by most tweeters. They, apparently, maintained a reasonable level of rationalization and analytical thinking capacities. Although certainty and confidence fluctuated over the fourteen studied weeks, the results also show that negative emotions, especially anxiety and anger, were steadily higher than usual. Questions about the readiness of this digital world to bear the consequences of COVID-19 and the possibility of avoiding the would-be inevitable COVID-X remain unanswered.
Although this research studied millions of tweets the English-speaking world has posted from January to April 2020, the topographical distribution of the tweeters was not equally distributed over the world. Therefore, the generated results, even though they represent the English-speaking world, should be viewed with much care for the non-English speaking world. We recommend that similar studies should be conducted to explore the multilingual divergence in conceptualizing the outbreak. We have also observed that media streams had a remarkable impact on perceiving the viral hazard. Agenda-setting and media-based studies should explore this finding further. We made our collected data available to all researchers for further investigation.