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PMC:7143846 JSONTXT 19 Projects

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Id Subject Object Predicate Lexical cue
T1 0-74 Sentence denotes The Impact of COVID-19 Epidemic Declaration on Psychological Consequences:
T2 75-104 Sentence denotes A Study on Active Weibo Users
T3 106-114 Sentence denotes Abstract
T4 115-227 Sentence denotes COVID-19 (Corona Virus Disease 2019) has significantly resulted in a large number of psychological consequences.
T5 228-508 Sentence denotes The aim of this study is to explore the impacts of COVID-19 on people’s mental health, to assist policy makers to develop actionable policies, and help clinical practitioners (e.g., social workers, psychiatrists, and psychologists) provide timely services to affected populations.
T6 509-688 Sentence denotes We sample and analyze the Weibo posts from 17,865 active Weibo users using the approach of Online Ecological Recognition (OER) based on several machine-learning predictive models.
T7 689-913 Sentence denotes We calculated word frequency, scores of emotional indicators (e.g., anxiety, depression, indignation, and Oxford happiness) and cognitive indicators (e.g., social risk judgment and life satisfaction) from the collected data.
T8 914-1091 Sentence denotes The sentiment analysis and the paired sample t-test were performed to examine the differences in the same group before and after the declaration of COVID-19 on 20 January, 2020.
T9 1092-1316 Sentence denotes The results showed that negative emotions (e.g., anxiety, depression and indignation) and sensitivity to social risks increased, while the scores of positive emotions (e.g., Oxford happiness) and life satisfaction decreased.
T10 1317-1412 Sentence denotes People were concerned more about their health and family, while less about leisure and friends.
T11 1413-1538 Sentence denotes The results contribute to the knowledge gaps of short-term individual changes in psychological conditions after the outbreak.
T12 1539-1802 Sentence denotes It may provide references for policy makers to plan and fight against COVID-19 effectively by improving stability of popular feelings and urgently prepare clinical practitioners to deliver corresponding therapy foundations for the risk groups and affected people.
T13 1804-1806 Sentence denotes 1.
T14 1807-1819 Sentence denotes Introduction
T15 1820-2003 Sentence denotes COVID-19 (Corona Virus Disease 2019) is a highly infectious disease with a long incubation period which was caused by Sars-Cov-2 (Severe Acute Respiratory Syndrome Coronavirus 2) [1].
T16 2004-2141 Sentence denotes The number of COVID-19 patients increased dramatically due to hundreds of millions of people traveling during the Spring Festival period.
T17 2142-2351 Sentence denotes The severity of COVID-19 had been underestimated until the National Health Commission classified it as a B type infectious disease officially and took actions to fight against this disease on 20 January, 2020.
T18 2352-2500 Sentence denotes Ever since then, epidemic prevention was comprehensively upgraded and marked the real beginning of universal concern, indicating widespread impacts.
T19 2501-2709 Sentence denotes The uncertainty and low predictability of COVID-19 not only threaten people’s physical health, but also affect people’s mental health, especially in terms of emotions and cognition, as many theories indicate.
T20 2710-2911 Sentence denotes According to Behavioral Immune System (BIS) theory [2], people are likely to develop negative emotions (e.g., aversion, anxiety, etc.) [3,4] and negative cognitive assessment [5,6] for self-protection.
T21 2912-3116 Sentence denotes Faced with potential disease threat, people tend to develop avoidant behaviors (e.g., avoid contact with people who have pneumonia-like symptoms) [7] and obey social norms strictly (e.g., conformity) [8].
T22 3117-3277 Sentence denotes According to stress theory [9] and perceived risk theory [10], public health emergencies trigger more negative emotions and affect cognitive assessment as well.
T23 3278-3374 Sentence denotes These negative emotions keep people away from potential pathogens when it refers to the disease.
T24 3375-3523 Sentence denotes However, long-term negative emotions may reduce the immune function of people and destroy the balance of their normal physiological mechanisms [11].
T25 3524-3707 Sentence denotes Meanwhile, individuals may overreact to any disease in case of less appropriate guidance from authorities, which may result in excessively avoidant behaviors and blind conformity [8].
T26 3708-3823 Sentence denotes Therefore, it is essential to understand the potential psychological changes caused by COVID-19 in a timely manner.
T27 3824-4138 Sentence denotes Since psychological changes caused by public health emergencies can be reflected directly in emotions and cognition [3,4,5,6], we can monitor psychological changes in time through emotional (e.g., negative emotions and positive emotions) and cognitive indicators (e.g., social risk judgment and life satisfaction).
T28 4139-4384 Sentence denotes The emotions and cognition are usually measured by retrospective questionnaires, such as Oxford Happiness Inventory (OHI) [12], Symptom Checklist 90 (SCL-90) [13], Satisfaction with Life Scale (SWLS) [14], and Likert Type Attitude Scale [15,16].
T29 4385-4681 Sentence denotes However, at the time of the COVID-19 outbreak in China, it was very difficult to conduct a traditional paper survey in the affected areas; online surveys rely on the cooperation of participants, and it is difficult to meet the requirements in time, and even brings extra burdens for participants.
T30 4682-4834 Sentence denotes Since we did not know the time of COVID-19 declaration, it was impossible to measure people’s emotions and cognition by a traditional survey in advance.
T31 4835-4938 Sentence denotes There may be a certain deviation when requiring people to recall their mental state a week or more ago.
T32 4939-5073 Sentence denotes Weibo data is emerging as a key online medium and data source for researchers to understand this social problem in a non-invasive way.
T33 5074-5189 Sentence denotes Sina Weibo is a leading Chinese Online Social Networks (OSN) with more than 462 million active daily users in 2019.
T34 5190-5308 Sentence denotes These users use Weibo functions (e.g., reply, @function) to interact with each other, forming rich user behavior data.
T35 5309-5614 Sentence denotes The aim of this study is to explore the impacts of public health emergency COVID-19 on people’s mental health, to assist policy makers to develop actionable policies, and help clinical practitioners (e.g., social workers, psychiatrists, and psychologists) provide services to affected populations in time.
T36 5616-5618 Sentence denotes 2.
T37 5619-5640 Sentence denotes Materials and Methods
T38 5642-5646 Sentence denotes 2.1.
T39 5647-5679 Sentence denotes Participants and Data Collection
T40 5680-5750 Sentence denotes The samples in this study were from the original Weibo data pool [17].
T41 5751-5817 Sentence denotes The data pool contained more than 1.16 million active Weibo users.
T42 5818-5951 Sentence denotes Weibo is a popular platform to share and discuss individual information and life activities, as well as celebrity news in China [18].
T43 5952-6058 Sentence denotes The retrieved data included (1) user’s profile information, (2) network behaviors, and (3) Weibo messages.
T44 6059-6153 Sentence denotes Privacy was strictly protected during the procedure, referring to the ethical principles [19].
T45 6154-6233 Sentence denotes We have obtained the Ethical Committee’s approval and the ethic code is H15009.
T46 6234-6329 Sentence denotes The following inclusion criteria were employed to select active Weibo users from the data pool.
T47 6330-6456 Sentence denotes First, they had published at least 50 original Weibo posts around a month in total from 31 December, 2019 to 26 January, 2020.
T48 6457-6542 Sentence denotes Second, their authentication type is non-institutional (e.g., individual user, etc.).
T49 6543-6619 Sentence denotes Third, their regional authentication is in China, not “overseas” or “other”.
T50 6620-6801 Sentence denotes We acquired 17,865 active Weibo users finally, then fetched all their original posts published during 13 January, 2020 to 26 January, 2020 into the two-week period for the analysis.
T51 6803-6807 Sentence denotes 2.2.
T52 6808-6858 Sentence denotes Measurement of Psychological Traits and Procedures
T53 6859-7112 Sentence denotes In this study, we used Online Ecological Recognition (OER) [20], which referred to the automatic recognition of psychological profile (e.g., anxiety, well-being, etc.) by using predictive models [17,20,21] based on ecological behavioral data from Weibo.
T54 7113-7377 Sentence denotes We employed Text Mind system developed by the Computational Cyber Psychology Laboratory at the Institute of Psychology, Chinese Academy of Sciences to extract content features [22], including Chinese word segmentation tool [17], and psychoanalytic dictionary [23].
T55 7378-7708 Sentence denotes We used the Chinese word segmentation tool to divide users’ original microblog content into words/phrases with linguistic annotations, such as verbs, nouns, adverbials, and objects [24], and then extracted psychologically meaningful categories through the simplified Chinese LIWC (Language Inquiry and Word Count) dictionary [23].
T56 7709-7778 Sentence denotes These lexical features were data sources for word frequency analysis.
T57 7779-7957 Sentence denotes After feature extraction, we used the psychological prediction model [25] obtained from the preliminary training to predict the psychological profile of these active Weibo users.
T58 7958-8249 Sentence denotes These predictive models are tools developed for online psychology research based on big data and deep learning technologies, including emotional indicators (anxiety, depression, indignation, and Oxford happiness), cognitive indicators (social risk judgment and life satisfaction), and so on.
T59 8250-8344 Sentence denotes Figure 1 portrays the procedure from feature extraction to psychological indicator prediction.
T60 8345-8433 Sentence denotes All the prediction models have reached a moderate correlation with questionnaire scores.
T61 8434-8515 Sentence denotes The feasibility of predictive models has been repeatedly demonstrated [26,27,28].
T62 8516-8781 Sentence denotes We calculated word frequency, scores of negative emotional indicators (i.e., anxiety, depression, and indignation), positive emotional indicators (i.e., Oxford happiness), and cognitive indicators (i.e., social risk and life satisfaction) of the collected messages.
T63 8782-9107 Sentence denotes We then compared the differences of psychological characteristics before and after the declaration of outbreak of COVID-19 on 20 January, 2020 through the paired sample t-test by using SPSS (Statistical Product and Service Solutions) 22, which is published by IBM (International Business Machines Corporation), New York, USA.
T64 9109-9111 Sentence denotes 3.
T65 9112-9119 Sentence denotes Results
T66 9121-9125 Sentence denotes 3.1.
T67 9126-9138 Sentence denotes Demographics
T68 9139-9274 Sentence denotes Among 17,865 active Weibo users, 25.23% were males and 77.95% were from Eastern China, which is considered the richest region in China.
T69 9275-9415 Sentence denotes Ages of users who registered their birth date in their profile (n = 4156, 23.26%) ranged from 8 to 56 years with the median age of 33 years.
T70 9416-9463 Sentence denotes The demographic profile is depicted in Table 1.
T71 9465-9469 Sentence denotes 3.2.
T72 9470-9491 Sentence denotes Linguistic Difference
T73 9492-9622 Sentence denotes In this study, we compare the LIWC categories between the week before (T-before) and after (T-after) 20 January, shown in Table 2.
T74 9623-9705 Sentence denotes It contains two types of LIWC categories: words of emotions and words of concerns.
T75 9706-9919 Sentence denotes Words of emotions include positive emotion (e.g., faith, contentment, and blessing), negative emotion (e.g., worry, suspicion, and jealousy), anxiety (e.g., upset, nervous, and crazy), and anger (e.g., complaint).
T76 9920-10298 Sentence denotes Words of concerns include health (e.g., insomnia, doctor, and exercise), leisure (e.g., cooking, chatting, and movies), family (e.g., family and house), friend (e.g., companion and guest), money (e.g., bills, cash, and borrowing), death (e.g., burial, killing, and funeral), and religion (e.g., church, mosque, and temple), which can reflect what people are paying attention to.
T77 10299-10500 Sentence denotes After 20 January, the number of words increased in positive emotion (t (17,747) = −24.411, p < 0.001), negative emotion (t (17,747) = −15.273, p < 0.001), and anxiety (t (17,747) = −15.294, p < 0.001).
T78 10501-10850 Sentence denotes Word frequency significantly increased in the category “concerns,” including health (t (17,747) = −72.392, p < 0.05), family (t (17,747) = −12.571, p < 0.001), death (t (17,747) = −6.707, p < 0.001), and religion (t (17,747) = −13.816, p < 0.001), but decreased in leisure (t (17,747) = 21.963, p < 0.001) and friend (t (17,747) = 6.202, p < 0.001).
T79 10852-10856 Sentence denotes 3.3.
T80 10857-10877 Sentence denotes Emotional Indicators
T81 10878-11037 Sentence denotes Results indicate significant differences of emotional indicators between T-before (13–19 January, 2020) and T-after (20–26 January, 2020), as shown in Table 3.
T82 11038-11382 Sentence denotes After 20 January, negative emotional indicators of psychological traits increased in anxiety (t (17,747) = −35.962, p < 0.001), depression (t (17,747) = −10.717, p < 0.001), and indignation (t (17,747) = 5.500, p < 0.001), while positive emotional indicators of psychological traits decreased in Oxford happiness (t (17,747) = 3.120, p < 0.01).
T83 11384-11388 Sentence denotes 3.4.
T84 11389-11409 Sentence denotes Cognitive Indicators
T85 11410-11561 Sentence denotes We found significant differences in cognitive indicators between T-before (13–19 January, 2020) and T-after (20–26 January, 2020), as shown in Table 4.
T86 11562-11759 Sentence denotes After 20 January, cognitive indicators of psychological traits increased in social risk judgement (t (17,747) = 3.120, p < 0.01), but decreased in life satisfaction (t (17,747) = 5.500, p < 0.001).
T87 11761-11763 Sentence denotes 4.
T88 11764-11774 Sentence denotes Discussion
T89 11775-11943 Sentence denotes Since the National Health Commission identified COVID-19 as a B type infectious disease officially, COVID-19 influenced the psychological states of people across China.
T90 11944-12051 Sentence denotes This study collected active Weibo users’ data, and conducted sentiment analysis during 13–26 January, 2020.
T91 12052-12207 Sentence denotes We used OER to acquire the psychological states, and found that Weibo users’ psychological conditions significantly changed under the outbreak of COVID-19.
T92 12208-12303 Sentence denotes The findings showed that people’s concerns by linguistic expression increased after January 20.
T93 12304-12388 Sentence denotes We observe an increase in health and family, while a decrease in leisure and friend.
T94 12389-12642 Sentence denotes Uncertainty of the upcoming situation causes cognitive dissonance and insecurity; this produces a feeling of mental discomfort, leading to Weibo’s activity oriented toward dissonance reduction and keeping security on health and family relationship [29].
T95 12643-12771 Sentence denotes According to the theory of BIS, people behave in a more reticent and conservative way when they feel threatened by disease [30].
T96 12772-12891 Sentence denotes Therefore, staying at home with family and reducing recreational activities seems to be a safer way to prevent illness.
T97 12892-13250 Sentence denotes It also indicated that people begin to care more about their health and were more likely to seek social support from their families rather than getting together with friends, which suggested that people’ interests and attention were influenced by the restricted travel policy and self-isolation regulations from the health authorities and central government.
T98 13251-13344 Sentence denotes Affected by COVID-19, messages related to death and religion became salient after 20 January.
T99 13345-13405 Sentence denotes Reports showed severity and potential mortality of COVID-19.
T100 13406-13586 Sentence denotes Research confirmed that people tended to respond to emergencies such as stress or death in the way of religion, which can comfort tense moods and bring more positive emotions [31].
T101 13587-13744 Sentence denotes That is why people prayed for the county through religion or other beliefs, leading to the phrase that appeared most frequently on the Internet at that time:
T102 13745-13761 Sentence denotes God bless China.
T103 13762-14036 Sentence denotes People showed more negative emotions (anxiety, depression, and indignation) and less positive emotions (Oxford happiness) after the declaration of COVID-19, which was supported by the theory of BIS, i.e., people did generate more negative emotions for self-protection [3,4].
T104 14037-14272 Sentence denotes These results are consistent to previous studies as well, which found that public health emergencies (e.g., SARS) triggered a series of stress emotional response containing a higher level of anxiety and other negative emotions [32,33].
T105 14273-14608 Sentence denotes Meanwhile, the confirmation that COVID-19 could be passed from person to person on 20 January, which was inconsistent with previous reports, lead to quite a number of people being unsatisfied with misinformation published from provincial governments (e.g., Hubei) and ineffective regulatory actions, causing an increase in indignation.
T106 14609-14764 Sentence denotes However, it’s worth noting that the word frequency of positive emotions increased after 20 January, which seemed to be inconsistent with the theory of BIS.
T107 14765-14941 Sentence denotes In fact, positive emotion includes words such as faith and blessing, which are more inclined to reflect group cohesiveness rather than pure personal emotions (e.g., happiness).
T108 14942-15174 Sentence denotes Researchers found that group threats (e.g., natural disasters and epidemic diseases) made groups a community of interests, resulting in more beneficial behaviors and social solidarity, which indicated higher group cohesiveness [34].
T109 15175-15339 Sentence denotes For example, lots of provinces (e.g., Sichuan Province, Shandong Province, etc.) formed medical teams to help the Hubei province, which was the worst affected area.
T110 15340-15433 Sentence denotes Many people donated money and supplies to Hubei Red Cross to support the control of COVID-19.
T111 15434-15546 Sentence denotes Furthermore, social risk judgement was higher and life satisfaction was lower after the declaration of COVID-19.
T112 15547-15907 Sentence denotes It is consistent with the theory of BIS, which found that when social uncertainty increased, such as unknown etiology and ambiguous route of transmission, people developed the negative cognitive assessment (e.g., higher sensitivity of risk judgment or risk perception) so that they could discover potential infection sources in time and avoid infection [2,35].
T113 15908-16076 Sentence denotes Not only that, people’s fear of potential risk and lack of controllability caused by COVID-19 brought about higher risk judgement as perceived risk theory claimed [10].
T114 16077-16248 Sentence denotes Moreover, some preventive policies and regulations in terms of travel restriction and self-isolation made the quality of life worse, reflecting in lower life satisfaction.
T115 16249-16450 Sentence denotes The following briefly foregrounds some of the study’s implications for policy makers and clinical practitioners (e.g., social workers, psychiatrists, and psychologists) plan and fight against COVID-19.
T116 16451-16621 Sentence denotes For policy makers: (1) develop a consistent policy and procedure for reporting the latest confirmed cases, recent death toll, and other data about the epidemic situation.
T117 16622-16782 Sentence denotes For example, the surge of cases on February 12th did not mean that the situation has been out of control, but because of the new diagnostic criteria introduced.
T118 16783-17032 Sentence denotes It is important to let people understand the data properly to reduce excessive stress responses (e.g., anxiety, depression, etc.) brought on by inappropriate perception. (2) Expand public awareness of continuous progress in decision-making measures.
T119 17033-17283 Sentence denotes Since indignation may come mainly from mistakes and deficiencies in preventing and controlling the epidemic, it can effectively decrease indignation if public awareness and involvement are provided. (3) Ensure the supply of medical treatment service.
T120 17284-17397 Sentence denotes It is critical to set up medical service to treat the disease, and let people know how to access it conveniently.
T121 17398-17447 Sentence denotes People can get help in time if they are infected.
T122 17448-17632 Sentence denotes It can improve people’s sense of control over risks, thereby avoiding excessive social risk perception. (4) Provide more in-door entertainment services to address good quality of life.
T123 17633-17776 Sentence denotes People may be more willing to cooperate when their living and entertainment requirements are met, such as online shopping, entertainments, etc.
T124 17777-17882 Sentence denotes For clinical practitioners: (1) adjust consultant configuration rationally and cooperate with each other.
T125 17883-18010 Sentence denotes Psychological consultants should grasp the epidemic information correctly and conduct science popularization during counseling.
T126 18011-18068 Sentence denotes Social workers can help solve practical problems in life.
T127 18069-18213 Sentence denotes These actions can improve the sense of stability and relieve anxiety and depression. (2) Deliver necessary psychosocial therapy in various ways.
T128 18214-18343 Sentence denotes Considering the particularity of self-isolation, relevant hotline counseling and online consulting should be applied in practice.
T129 18344-18426 Sentence denotes Several other points should be considered when generalizing this study’s findings.
T130 18427-18515 Sentence denotes First, as Weibo users are mainly young people, the results may be biased to some extent.
T131 18516-18716 Sentence denotes In addition, the current analysis is based on a weekly basis, with a relatively large granularity, which has certain influences on reflecting the changing trend of social mentality in a timely manner.
T132 18717-18840 Sentence denotes In further studies, we will try to expand the range of sex and age and predict psychological traits in a finer granularity.
T133 18841-18998 Sentence denotes Previous studies indicated that people tended to exaggerate attitudes and prejudices, especially when they felt more vulnerable to disease transmission [36].
T134 18999-19222 Sentence denotes It inspires us to try to build a prediction model which can predict people’s attitudes and beliefs against the virus through online Weibo data for further understanding of psychological impacts of public health emergencies.
T135 19224-19226 Sentence denotes 5.
T136 19227-19238 Sentence denotes Conclusions
T137 19239-19365 Sentence denotes In this study, we compared the difference before and after 20 January on both linguistic categories and psychological profile.
T138 19366-19603 Sentence denotes We found an increase in negative emotions (anxiety, depression, and indignation) and sensitivity to social risks, as well as a decrease in positive emotions (Oxford happiness) and life satisfaction after declaration of COVID-19 in China.
T139 19604-19706 Sentence denotes What’s more, people show more concern for health and family, and less concern for leisure and friends.
T140 19707-19864 Sentence denotes Using social media data may provide timely understanding of the impact of public health emergencies on the public’s mental health during the epidemic period.
T141 19866-19886 Sentence denotes Author Contributions
T142 19887-19943 Sentence denotes S.L., N.Z., and T.Z. conceived and planned this article.
T143 19944-20012 Sentence denotes S.L. and Y.W. carried out the search and revision of the literature.
T144 20013-20050 Sentence denotes T.Z. collected and provided the data.
T145 20051-20083 Sentence denotes S.L. and Y.W. analyzed the data.
T146 20084-20107 Sentence denotes S.L. drafted the study.
T147 20108-20161 Sentence denotes J.X., N.Z., and T.Z. reviewed and edited the writing.
T148 20162-20275 Sentence denotes All authors (S.L., Y.W., J.X., N.Z., and T.Z.) revised the article critically for important intellectual content.
T149 20276-20418 Sentence denotes All authors (S.L., Y.W., J.X., N.Z., and T.Z.) commented on and approved the final manuscript and are accountable for all aspects of the work.
T150 20419-20495 Sentence denotes All authors have read and agreed to the published version of the manuscript.
T151 20497-20504 Sentence denotes Funding
T152 20505-20601 Sentence denotes This research was funded by National Natural Science Foundation of China, grant number 31700984.
T153 20603-20624 Sentence denotes Conflicts of Interest
T154 20625-20670 Sentence denotes The authors declare no conflicts of interest.
T155 20672-20796 Sentence denotes Figure 1 Procedures of feature extraction from online Weibo data and psychological indicator predicted by dynamic features.
T156 20797-20859 Sentence denotes Table 1 Demographic characteristics of selected participants.
T157 20860-20865 Sentence denotes n (%)
T158 20866-20892 Sentence denotes Gender male 4507 (25.23)
T159 20893-20915 Sentence denotes female 13,358 (74.77)
T160 20916-20935 Sentence denotes Age –9 110 (0.62)
T161 20936-20952 Sentence denotes 10–19 20 (0.11)
T162 20953-20972 Sentence denotes 20–29 2035 (11.39)
T163 20973-20991 Sentence denotes 30–39 1598 (8.94)
T164 20992-21007 Sentence denotes 40– 393 (2.20)
T165 21008-21036 Sentence denotes missing data 13,709 (76.74)
T166 21037-21086 Sentence denotes Region of location Eastern China 13,925 (77.95)
T167 21087-21113 Sentence denotes Central China 1644 (9.20)
T168 21114-21141 Sentence denotes Western China 2296 (12.85)
T169 21142-21162 Sentence denotes Total 17,865 (100)
T170 21163-21224 Sentence denotes Table 2 Word frequency analysis before and after 20 January.
T171 21225-21253 Sentence denotes T-Before T-After t df p
T172 21254-21266 Sentence denotes M SD M SD
T173 21267-21284 Sentence denotes Words of emotions
T174 21285-21353 Sentence denotes Positive emotion 2.58 1.46 2.86 1.47 −24.411 17,747 0.000 ***
T175 21354-21422 Sentence denotes Negative emotion 0.71 0.63 0.79 0.59 −15.273 17,747 0.000 ***
T176 21423-21482 Sentence denotes Anxiety 0.09 0.17 0.12 0.17 −15.294 17,747 0.000 ***
T177 21483-21535 Sentence denotes Anger 0.19 0.26 0.19 0.23 −0.347 17,747 0.792
T178 21536-21553 Sentence denotes Words of concerns
T179 21554-21612 Sentence denotes Health 0.37 0.43 0.72 0.63 −72.392 17,747 0.000 ***
T180 21613-21671 Sentence denotes Leisure 1.77 1.28 1.60 1.19 21.963 17,747 0.000 ***
T181 21672-21730 Sentence denotes Family 0.22 0.30 0.25 0.30 −12.571 17,747 0.000 ***
T182 21731-21787 Sentence denotes Friend 0.11 0.20 0.10 0.16 6.202 17,747 0.000 ***
T183 21788-21839 Sentence denotes Money 0.71 0.77 0.71 0.75 1.353 17,747 0.176
T184 21840-21896 Sentence denotes Death 0.14 0.27 0.15 0.24 −6.707 17,747 0.000 ***
T185 21897-21957 Sentence denotes Religion 0.28 0.46 0.32 0.45 −13.816 17,747 0.000 ***
T186 21958-22165 Sentence denotes T-before represents the word frequency during 13–19 January, 2020; T-after represents the word frequency during 20–26 January, 2020; M = mean; SD = standard deviation; df = degrees of freedom. *** p < 0.001.
T187 22166-22224 Sentence denotes Table 3 Emotional indicators before and after 20 January.
T188 22225-22253 Sentence denotes T-Before T-After t df p
T189 22254-22266 Sentence denotes M SD M SD
T190 22267-22284 Sentence denotes Negative emotions
T191 22285-22346 Sentence denotes Anxiety 11.69 4.61 12.79 4.66 −35.962 17,747 0.000 ***
T192 22347-22411 Sentence denotes Depression 14.87 4.81 15.27 5.08 −10.717 17,747 0.000 ***
T193 22412-22475 Sentence denotes Indignation 1.83 0.43 1.86 0.45 −11.415 17,747 0.000 ***
T194 22476-22493 Sentence denotes Positive emotions
T195 22494-22561 Sentence denotes Oxford happiness 89.91 9.48 89.71 8.84 3.120 17,747 0.002 **
T196 22562-22814 Sentence denotes T-before represents the predicted emotional indicators during 13–19 January, 2020; T-after represents the predicted emotional indicators during 20–26 January, 2020; M = mean; SD = standard deviation; df = degrees of freedom. ** p < 0.01, *** p < 0.001.
T197 22815-22873 Sentence denotes Table 4 Cognitive indicators before and after 20 January.
T198 22874-22902 Sentence denotes T-Before T-After t df p
T199 22903-22915 Sentence denotes M SD M SD
T200 22916-22987 Sentence denotes Social risk judgment 4.10 0.27 4.12 0.25 −8.832 17,747 0.000 ***
T201 22988-23057 Sentence denotes Life satisfaction 14.33 2.47 14.24 2.28 5.500 17,747 0.000 ***
T202 23058-23297 Sentence denotes T-before represents the predicted cognitive indicators during 13–19 January, 2020; T-after represents the predicted cognitive indicators during 20–26 January, 2020; M = mean; SD = standard deviation; df = degrees of freedom. *** p < 0.001.