Social Sharing of Emotion During the COVID-19 Pandemic

Abstract

The long-term uncertainty and persistence of isolation caused by the COVID-19 pandemic created prolonged emotional distress individually and collectively. As the pandemic progressed, the dynamic ride of emotional experience was expressed live and shared online, particularly on social media. In this study, we collected posted messages on Twitter for a longitudinal investigation to determine how emotional experiences changed over time during the pandemic. In total, we analyzed 41,868,013 COVID-19-related tweets in English posted from January 21 to July 31, 2020. Using a stage model, the results demonstrated that there were three stages during the pandemic characterized by distinct emotional changes. The first stage features high anxiety and negative emotions compared with the other stages, possibly due to the lack of information about the disease. The second stage shows the dynamic ride of all emotions and an increase in negative emotions (particularly anger) as the COVID-19 pandemic proceeds. In the third stage, most emotions are stabilized, except for depression, despite the protracted pandemic.

Introduction

COVID-19, a new epidemic that began in China in 2019, has affected the health, safety, and wellbeing of individuals and communities worldwide. In the United States, the disease had caused more than 74 million confirmed cases with 0.8 million deaths as of January 2022. The implications of the pandemic are not confined to virus-related health issues. Social distancing, isolation, infringement of personal freedoms, financial losses, fake news/misinformation, radical shifts at work and school, shortage of medical resources, and uncertainty in the prognoses of the unknown disease have created prolonged emotional distress individually and collectively.^1,2 In general, the public is experiencing negative emotions, including anger, stress,^3,4 fear,⁵ and anxiety.⁶ Psychological wellbeing is at stake with an increased risk of psychiatric illness, including depression,⁷ mental disorders, and suicide.^1,8

As the pandemic has progressed, the dynamic ride of emotional experience has been shared online, particularly on social media. Social media provides a convenient interface where people can easily express their emotions and efficiently share them with a large number of people. As Rimé stated,⁹ emotions are socially shared through words, and people have shown a pattern of active social sharing of emotions through the use of media. Ellis et al.¹⁰ and Liet al.¹¹ observed a surge in the number of social media messages in the physically isolated situation caused by COVID-19.

Emotional experiences during the COVID-19 pandemic expressed on social media have been investigated in several studies.^12–14 For example, Saha et al.¹⁵ compared Twitter posts before and after the COVID-19 pandemic to show that anxiety, depression, stress, suicidal ideation, and support to others increased during the COVID-19 period. Crocamo et al.¹⁴ conducted sentiment analysis on longitudinal Twitter data (from January 19 to March 3, 2020) and revealed that the amount of both positive and negative emotions increased, and a proportion of negative emotions was particularly pronounced after the occurrence of key events, such as the pandemic declaration by the World Health Organization (WHO) and the rapid increase of cases in Europe. Yet, there is still a dearth of information regarding the patterns of emotional experiences.

Thus, we have three purposes in this study: (a) to quantitatively measure the various emotions and mental health-related indicators that were shared on social media, (b) to analyze whether there are any patterns of emotional changes in the early period of the pandemic based on the stage model of how humans react to unfortunate events, and (c) to derive practical guidelines to better prepare for the public's emotional response based on the observed emotional trajectory.

Literature Review

Social sharing of emotion on social media

It is a natural behavior to share one's emotions with others, whether the emotions are positive or negative.¹⁶ Rimé et al. defined social sharing of emotion as an interpersonal process in which a narrator shares emotions about their experience of specific situations with others.^17,18 Emotionally arousing information is especially likely to be shared with others.¹⁹ Recent studies have demonstrated that the valence of emotion also matters. Positive emotions tend to promote social sharing^20,21 because individuals are motivated to manage their online impressions by sharing positive posts.²²

However, in a public health crisis such as a pandemic, sharing negative emotions may become more common,²⁰ as individuals easily presume that many people are concurrently experiencing negative emotions and may think sharing such emotions will not harm their online impression. In addition, social sharing of emotions provides several benefits. Talking about stressful events significantly decreases psychological distress^23,24 while increasing emotional relief.²⁵ Compared with restrained emotional conditions, people who express their emotions report a lower level of perceived stress.²⁶ Furthermore, sharing emotions about traumatic experiences or major negative life events, such as the death or illness of a close person or a serious accident, helps people to cope both cognitively and socio-affectively with the psychological distress caused by the event.²⁷ Sharing emotions allows people to gain empathy and sympathy because receiving attention, feedback, support, or validation²⁸ strengthens social bonds and support.²⁷

Explaining the pandemic using a stage model

Individuals go through a series of emotional stages in the face of unexpected and unfortunate events, such as terminal illness, natural disaster, or loss of loved ones.^29–33 Many researchers have attempted to explain the process of devastating experiences in terms of emotional stages. For example, in a study of parents who lost their child, Davidson³⁴ proposed a four-dimensional model of the grieving process: shock and numbness, yearning and searching, disorientation, and reorganization. Rando³⁵ argues that there are three dynamic stages with varying fluctuations and properties: avoidance, confrontation, and accommodation.

Among many stage models, Kübler-Ross's five stages of grief^31,36,37 is perhaps the most widely known model that describes the stages of emotional change. It outlines the process of accepting the situation for patients who are about to die or individuals who have experienced bereavement or loss. The model comprises five stages of emotions: denial, anger, bargaining, depression, and acceptance. Denial is the stage that deals with grief and distrust about what has happened. In the anger stage, individuals look for someone or something to blame while questioning the fairness of their situation. The bargaining stage is also referred to as the trade-off stage. Individuals seek to make a deal with fate to earn more time with lost loved ones. Individuals in the depression stage experience extreme sorrow and helplessness. In the acceptance stage, the individuals have a sense of understanding and can continue on their grief journey.³⁸ However, not all grief-stricken individuals have to go through this process the same way or follow a set of sequence. Some individuals go through all stages, but others can skip one or more steps or go back to previous stages.³⁹

Recent studies have expanded the use of the stage model to explain situations beyond personal loss or an individual's experience. For example, Whaley⁴⁰ explained how Hurricane Katrina survivors deal with a natural disaster and the related trauma. Pennebaker and Harber⁴¹ suggested a social stage model of collective coping by studying individuals who experienced the Loma Prieta Earthquake and the Persian Gulf War. They showed that collective coping stage had three phases: emergency, inhibition, and adaptation.

There have been a few attempts to explain emotional experience during the COVID-19 pandemic by looking at COVID-19-related news,⁴² medical school students and professors' reactions,⁴³ and official White House documents.⁴⁴ However, similar to previous stage-based studies that are largely based on qualitative observations, there is a lack of quantitative evidence for the stage model for the COVID-19 pandemic.

Recently, Li et al.⁴⁵ conducted a four-wave survey in both China and the United States to investigate the relationship between emotion, information acquisition, and preventive behaviors. They identified four stages: prepandemic, onset of viral outbreak, ongoing risk, and contained risk. As their study was conducted using a survey, it has methodological limitations that any survey studies would have. The survey is based on the answers to the survey questions prepared by the author, so the information may be limited to the structure of the questionnaire. In addition, the data collection points are predetermined by the researchers, so the period of stages has to be confined to the data collection points and may not reflect the actual period of stages.

To fill the gap in the literature, this study conducted a computational analysis to examine messages on Twitter in a longitudinal investigation to determine how emotional experiences changed over time during the pandemic. The research question that guided this study is proposed as follows:

RQ: How do emotions and mental health status change over time during the pandemic?

Materials and Methods

Data collection

COVID-19-related tweets posted on Twitter during the COVID-19 outbreak were collected for this study. We used the open-source Twitter API to collect tweets that included COVID-19-related terms, such as “COVID” or “corona,” from January 21 to July 31, 2020. We excluded tweets that were not written in English. Our final dataset contained 38,226,795 tweets. Table 1 summarizes the data used in this study. This cross-sectional study was approved by the Institute Review Board (IRB No. 2020-10-021). Informed consent from participants was exempted as all the data are publicly available, and the personal information such as usernames was anonymized.

Table 1.

Dates, Total Number of Tweets, and Average Number of Words During the COVID-19 Outbreak

	During COVID-19
Period	January 21, 2020 to July 31, 2020
Total No. of tweets	38,226,795
Average No. of words per tweets	18.98

Data preprocessing

To clean the text data, we removed emojis, special characters such as “#” or “*,” and URL links in each tweet. To analyze the sentiments revealed in tweets each day, we divided the data into 192 days' data for 2020. This retrospective study did not require approval from an Ethics Committee and did not violate the terms of the Twitter platform.

Data analysis

Sentiment analysis

We performed sentiment analysis on the collected tweet data using Linguistic Inquiry and Word Count (LIWC).⁴⁶ LIWC is a dictionary-based tool that can analyze grammatical, psychological, and content word categories for a given text. Following simplistic working principles, this tool provides researchers with an automated and objective way to extract insights through language.⁴⁷ The LIWC dictionary used in this article is the LIWC2015 version, which consists of a total of 6,549 labeled words and word stems.⁴⁸ LIWC codes each word or word stem based on one or more word categories. For example, the word “cried” receives scores in four-word categories: sadness, negative emotion, overall affect, and a past tense verb.⁴⁹ Hence, if the word is found in the target text, each of these four category scores will be incremented. In our analysis, the following psychological categories provided in LIWC were analyzed: positive and negative emotions, anger, anxiety, and sadness.

Mental health state analysis

In addition to emotions, mental health states, such as stress, depression, and suicidal ideation, were analyzed. We calculated the stress score using an additional customized LIWC dictionary.⁵⁰ For the suicidal ideation score, we applied a previously developed suicidal ideation detection model⁵¹ that is designed to identify whether a given social media post includes suicidal ideation by using suicide-oriented word embeddings and attention mechanisms.⁵² To calculate the depression score for each tweet, we developed a depression classifier that identifies whether a given text includes a depression state. To learn the context of depression from the text, we collected posts of the subreddits related to depressive disorder from Reddit, including r/bipolar, r/BipolarReddit, and r/BipolarSOs, which are associated with the depressive disorder category⁵³ in the Diagnostic and Statistical Manual of Mental Disorders Fifth Edition (DSM-5). The proposed depression classifier was trained using pretrained language models such as Word2vec⁵⁴ and fastText⁵⁵ for post representation by applying long short-term memory.⁵⁶

Results

To answer the research question, we investigated how emotion and mental health indicators changed over time during the pandemic. The pattern of shared emotion changed significantly on the basis of two important dates during the pandemic: the WHO's official declaration of the global pandemic (March 11, 2020) and the easing of the shutdown policy (June 10, 2020). Based on these two dates, the pandemic was divided into three periods. Overall, the result demonstrates the significant changes of emotions and mental health indicators across the three stages.

The first stage extended from the first confirmed case of COVID-19 (January 21, 2020) to the WHO's official declaration of the global pandemic (March 11, 2020). After COVID-19 was first identified in December 2019 in Wuhan, China, the situation quickly worsened, and the U.S. government became cautious about the situation. When the first confirmed case was reported, it triggered a burst of uncertainty about the unfamiliar disease, arousing greater anxiety. At this stage, the number of confirmed cases had been growing gradually, but the major pandemic had yet to come.

This stage is characterized by a high level of anxiety (Fig. 1). Among all the emotions, anxiety was the only sentiment that was significantly higher than the ones in the other stages (Fig. 2). Tweets in this period included: “We are panicking too much over Coronavirus, but the panic is something to panic about.” and “Potential Coronavirus scares are the new potential terrorist plots. Scary times and false alarms will abound.” It is unique in that negative emotions prevail over positive emotions, unlike in other stages. More accurately, negative emotions were more pronounced in this stage because the positive emotion scores were significantly lower than in the other stages (Fig. 2).

FIG. 1.

Sentiments, mental health-related scores, and the U.S. COVID-19 daily confirmed cases for each stage. ANG, anger; ANX, anxiety; DEP, depression; NEG, negative emotion; POS, positive emotion; SAD, sadness; SCI, suicidal ideation; STR, stress.

FIG. 2.

Bar plot for all sentiments and mental health states for each stage (NS stands for groups with nonsignificant results, p > 0.05).

The second stage started on the day after the first stage and ended June 9, 2020, when the lockdown was declared. This period started with President Donald Trump's declaration of a national emergency (March 13), and a variety of social distancing measures were implemented throughout the United States; most of the states in America announced the stay-at-home order and the level-4 travel advisory that asked citizens not to travel around the world. In about a month, the United States ranked number one in COVID-19 deaths worldwide, and all 50 states declared an emergency. To make matters worse, there were confusing guidelines from the government regarding mask regulations and ways to prevent the virus (e.g., bleach and sunshine).

During this stage, there was a sharp increase in anger and dynamic emotional ride (Fig. 1) Anger, a highly negative emotion, showed the most prominent change. The average score of anger was high and the level of anger fluctuated on a daily basis. For example, individuals mentioned: “I am so angry today. Angry at COVID, angry at racist murdering cops, angry at those who refuse to wear masks,” “The stupidity surrounding COVID-19 is literally making me so mad. People are so annoying, so dumb, and so greedy,” and “stupid coronavirus locking everyone inside. stupid stupid.” In addition, both negative and positive emotions show deep ups and downs to show dynamic emotional changes during this period. Peak stress and depression scores were also found in this period. Figure 2 shows a considerable increase in anger, sadness, depression, positive emotions, and suicidal ideation in stage 2 compared with stage 1. In particular, anger, stress, suicidal ideation, and positive emotions were significantly higher than in the other periods. At the same time, the heightened level of anxiety decreased during this stage.

Finally, in the third stage after the lockdown policy was eased in most states, emotions remained stable as people adapted to the “new normal.” Sample tweets during this period include: “For the past 2 months I've spent Thursday nights helping facilitate a weekly COVID-19 town hall webinar for community pediatricians. We're spacing those out as the pandemic reaches a new equilibrium locally. We ended each meeting in a moment of gratitude.,” “We have been looking for the silver lining in this dark, depressing, and dire COVID19 cloud, more compassion may well be it.,” and “Thank God for summer! I have to admit, after a bleak, cold, depressing no good winter, spring, and coronavirus nonsense, didn't expect the economy to recover so quickly. I'd been expecting traumatized consumers and skittish businesses. But maybe not.”

Similarly, the quantitative data also indicated a stabilized pattern. Most indicators showed a significant decrease in negative emotion during the third stage. Stress, anxiety, and negative emotions showed the lowest levels at this stage, and stress showed the biggest drop. Positive emotions also decreased and stabilized. Unlike other indicators, depression did not decrease significantly; it had increased significantly from the first stage to the second stage, and the heightened level of depression remained almost the same in the third stage. As COVID-19 continued to proliferate, individuals seemed to be adjusting to their new daily life. Issues unrelated to COVID-19, such as the Black Lives Matter movement and the Hong Kong protests, received more attention. In this period, even though the number of confirmed COVID-19 cases almost doubled, all emotions and mental health-related indicators remained stable without a drastic change. People tended to express relatively low levels of negative emotions during the last stage.

Discussion

Overall discussion

This study investigated emotional experiences shared on Twitter during the COVID-19 pandemic. The results showed dynamic changes in sentiments over time. For 6 months starting from the day when the first confirmed case was reported in the United States, there were clear stages showing distinct emotional patterns from each other.

In the first stage, anxiety levels were significantly high. Many individuals expressed anxious feelings about a relatively unknown disease and what would happen in the future. This finding supports the strong relationship between uncertainty and anxiety as demonstrated in studies conducted in various contexts.^57,58 Noticeably, negative emotions significantly surpassed positive emotions in this period. This pattern has rarely been observed in previous studies on social media. Social media users tend to post positive messages in an effort to manage their online impression,²² and positive messages are more likely to be retweeted.²⁰ Nevertheless, our findings showed that negative emotions were quite stably high during the pandemic, while positive emotion was low in stage 1. In stage 2, although the numbers of both deaths and confirmed cases rose, positive emotions increased to a surprising degree. It is interesting that positive emotions were suppressed more before than after something bad actually happened.

In the second stage, anger scored prominently high. It was the emotional indicator that increased the most, perhaps due to the insurmountable pandemic situation and insufficient government responses. This finding aligns with Kübler-Ross's model, which identifies the second stage as anger and fixating on asking who is to blame and why the situation happened. Similarly, during the pandemic, there were widespread riots, protests, hatred toward specific groups, and criticism of the government. As more information about the pandemic was revealed, individuals seemed to start asking, “Why this is happening to me?” Nevertheless, while all emotional indicators significantly increased, anxiety decreased, perhaps because anxiety is related to uncertainty and anticipation about something that has not yet occurred. That is, as more information about COVID-19 was revealed in stage 2, anxiety subsided.

In the last stage, most emotions show a stabilized pattern, indicating that individuals started to accept the “new normal.” Most emotions were significantly reduced in stage 3; however, depression continued to be at a similarly high level. In our study, depression was high in stage 2, but it was not very prominent because other emotions were greatly heightened. Depression was more visible in stage 3, when all other emotions subsided. Similarly, in Kübler-Ross's model, depression and acceptance are the last two stages in which people finally accept the situation. Stage models have consistently suggested that individuals accept the new situation in the final stage, although these stages may have different names such as reorganization,³⁴ accommodation,⁵⁹ or adaptation.⁴¹

Implications

This study has important theoretical and practical implications. The findings make a theoretical contribution by adding quantitative evidence for the stage model. As many stage model-based investigations have largely relied on case studies or qualitative observations, it is meaningful that this study demonstrates quantitative evidence for emotional experiences. In addition, this study expands the application of the stage model by studying a fairly unique context. The experience of the COVID-19 pandemic situation may be largely different from that of personal loss in that the latter is individual and the former is collective.

In addition, the COVID-19 pandemic is different even from other contexts of collective experience, such as natural disasters, in that the emotional experience affected not just some people but virtually everyone. For example, Pennebaker and Harber's⁴¹ social stage model of coping explains that there is a social dilemma in talking about an upheaval because the disaster was experienced only by the victims, not by the listeners. While victims of trauma from a natural disaster want to share their experiences with others as a way to release their stress, disclosure is often deterred because of “listeners' efforts to maintain emotional repose.” Thus, sharing the emotional experience would be very much different when all societies experience the pandemic together. Our study analyzed publicly expressed and shared emotions on social media and we were able to apply the data into a stage model to build a helpful framework to understand the patterns of socially shared emotions on social media during the pandemic.

Our findings provide practical implications for helping medical professionals and government officials to predict the emotional response of the public, which can serve as the basis of guidelines for making plans and policies in crisis situations such as a pandemic. In particular, as other studies revealed that mental health issues gained importance during the pandemic,^60–62 being able to predict how the public would react ahead of time would help future planning of related countermeasures.

Limitations

This study has several limitations. First, emotional experiences may differ depending on societal factors, such as government responses and policies⁶³ and cultural factors. It would be meaningful to conduct an analysis using social media posts in different languages and about events occurring in different countries. We believe that the stage model can become more robust in light of the analysis of quantitative data collected from various societies. Second, this study analyzed the “expressed and shared” emotions revealed in social media; hence, understanding of “unexpressed and unshared” voices was limited. For instance, our results showed a relatively low score for depression. Perhaps individuals with depressive emotions may feel too lethargic to post something on social media. In addition, individuals or their families with COVID-19 may feel stigmatized and refrain from sharing their emotions openly on social media.

Footnotes

Author Disclosure Statement

We have no conflicts of interest to disclose.

Funding Information

This research was supported by the framework of international cooperation program managed by the National Research Foundation of Korea (NRF-2020K2A9A2A11103842) and the MSIT (Ministry of Science and ICT), Korea, under the ICAN (ICT Challenge and Advanced Network of HRD) program (IITP-2021-2020-0-01816) supervised by the IITP (Institute of Information & Communications Technology Planning & Evaluation).

References

Pfefferbaum

, North

. Mental health and the Covid-19 pandemic. New England Journal of Medicine, 2020; 383:510–512.

Masiero

, Mazzocco

, Harnois

, et al. From individual to social trauma: sources of everyday trauma in Italy, the US and UK during the COVID-19 pandemic. Journal of Trauma & Dissociation, 2020; 21:513–519.

Bao

, Sun

, Meng

, et al. 2019-nCoV epidemic: address mental health care to empower society. The Lancet, 2020; 395:e37–e38.

Bakioğlu

, Korkmaz

, Ercan

. Fear of COVID-19 and positivity: mediating role of intolerance of uncertainty, depression, anxiety, and stress. International Journal of Mental Health and Addiction, 2021; 19:2369–23824.

Pakpour

, Griffiths

. The fear of COVID-19 and its role in preventive behaviors. Journal of Concurrent Disorders, 2020; 2:58–63.

Sood

. Psychological effects of the Coronavirus disease-2019 pandemic. Research & Humanities in Medical Education, 2020; 7:23–26.

Wang

, Pan

, Wan

, et al. Immediate psychological responses and associated factors during the initial stage of the 2019 coronavirus disease (COVID-19) epidemic among the general population in China. International Journal of Environmental Research and Public Health, 2020; 17:1729.

Hwang

T-J

, Rabheru

, Peisah

, et al. Loneliness and social isolation during the COVID-19 pandemic. International Psychogeriatrics, 2020; 32:1217–1220.

Rimé

. (2005) The social sharing of emotions [in French]. Paris: Presses Universitaires de France.

10.

Ellis

, Dumas

, Forbes

. Physically isolated but socially connected: psychological adjustment and stress among adolescents during the initial COVID-19 crisis. Canadian Journal of Behavioural Science/Revue canadienne des sciences du comportement, 2020; 52:177.

11.

, Li

, et al. (2020) What are we depressed about when we talk about covid-19: Mental health analysis on tweets using natural language processing. In International Conference on Innovative Techniques and Applications of Artificial Intelligence. Cham: Springer, pp. 358–370.

12.

Valdez

, Ten Thij

, Bathina

, et al. Social media insights into US mental health during the COVID-19 pandemic: longitudinal analysis of Twitter data. Journal of Medical Internet Research, 2020; 22:e21418.

13.

Xue

, Chen

, Hu

, et al. Twitter discussions and concerns about COVID-19 pandemic: twitter data analysis using a machine learning approach. arXiv preprint arXiv:200512830. 2020.

14.

Crocamo

, Viviani

, Famiglini

, et al. Surveilling COVID-19 Emotional Contagion on Twitter by Sentiment Analysis. European Psychiatry, 2021; 64:e17.

15.

Saha

, Torous

, Caine

, et al. Social media reveals psychosocial effects of the COVID-19 pandemic. medRxiv 2020.

16.

Pennebaker

, Zech

, Rimé

. (2001) Disclosing and sharing emotion: psychological, social, and health consequences. In Stroebe

, Hansson

, Stroebe

, et al., eds. Handbook of bereavement research: Consequences, coping, and care. Washington, DC: American Psychological Association, pp. 517–539.

17.

Rimé

, Mesquita

, Boca

, et al. Beyond the emotional event: six studies on the social sharing of emotion. Cognition & Emotion, 1991; 5:435–465.

18.

Rimé

, Finkenauer

, Luminet

, et al. Social sharing of emotion: new evidence and new questions. European Review of Social Psychology, 1998; 9:145–189.

19.

Rimé

. (1995) Mental rumination, social sharing, and the recovery from emotional exposure. In Pennebaker

, ed. Emotion, disclosure, & health. Washington, DC: American Psychological Association, pp. 271–291.

20.

Berger

, Milkman

. What makes online content viral?. Journal of Marketing Research, 2012; 49:192–205.

21.

, Prestin

, Lee

, et al. What do people like to “share” about obesity? A content analysis of frequent retweets about obesity on Twitter. Health Communication, 2016; 31:193–206.

22.

Wojnicki

, Godes

. Word-of-mouth as self-enhancement. HBS Marketing Research Paper, 2008; 6:1.

23.

Pennebaker

, Beall

. Confronting a traumatic event: toward an understanding of inhibition and disease. Journal of Abnormal Psychology, 1986; 95:274.

24.

Mendolia

, Kleck

. Effects of talking about a stressful event on arousal: does what we talk about make a difference?. Journal of Personality and Social Psychology, 1993; 64:283.

25.

Pennebaker

. Confession, inhibition, and disease. Advances in Experimental Social Psychology, 1989; 22:211–244.

26.

Lepore

, Ragan

, Jones

. Talking facilitates cognitive–emotional processes of adaptation to an acute stressor. Journal of Personality and Social Psychology, 2000; 78:499.

27.

Rimé

. Emotion elicits the social sharing of emotion: theory and empirical review. Emotion Review, 2009; 1:60–85.

28.

Rimé

. Interpersonal emotion regulation. Handbook of Emotion Regulation, 2007; 1:466–468.

29.

Lindemann

. Symptomatology and management of acute grief. American Journal of Psychiatry, 1944; 101:141–148.

30.

Bowlby

. Processes of mourning. International Journal of Psycho-Analysis, 1961; 42:317–340.

31.

Oken

. On death and dying: what the dying have to teach doctors, nurses, clergy and their own families. Archives of General Psychiatry, 1969; 21:639.

32.

Morrow

. (2022) DABDA: the 5 stages of coping with death. Verywell Health. https://www.verywellhealth.com/dabda-the-five-stages-of-coping-with-death-1132148 (accessed Apr. 4, 2022).

33.

Hierholzer

, Bellamy

, Mannix

. (2015) Supplemental research bulletin: traumatic stress and suicide after disasters. Rockville, MD: SAMHSA (Substance Abuse and Mental Health Services Administration) Disaster Technical Assistance Center, pp. 1–20.

34.

Davidson

. Death of the wished-for child: a case study. Death Education, 1977; 1:265–275.

35.

Rando

. (1988) Grieving: how to go on living when someone you love dies. Lexington, MA: Lexington Books.

36.

Kübler-Ross

, Kessler

. (2005) On grief and grieving: finding the meaning of grief through the five stages of loss. New York, NY: Scribner.

37.

Kubler-Ross

. On death and dying. Southern Medical Journal, 1971; 64:641.

38.

Scott

. (2020) The 6 stages of coronavirus grief: COVID-19. https://www.ednc.org/perspective-the-6-stages-ofcoronavirus-grief (accessed Dec. 4, 2020).

39.

Kessler

. (2019) Finding meaning: the sixth stage of grief. Simon and Schuster. New York, NY: Scribner.

40.

Whaley

. Trauma among survivors of Hurricane Katrina: considerations and recommendations for mental health care. Journal of Loss and Trauma, 2009; 14:459–476.

41.

Pennebaker

, Harber

. A social stage model of collective coping: the Loma Prieta earthquake and the Persian Gulf War. Journal of Social Issues, 1993; 49:125–145.

42.

Mortazavi

, Assari

, Alimohamadi

, et al. Fear, loss, social isolation, and incomplete grief due to COVID-19: a recipe for a psychiatric pandemic. Basic and Clinical Neuroscience, 2020; 11:225.

43.

Tempski

, Danila

, Arantes-Costa

, et al. The COVID-19 pandemic: time for medical teachers and students to overcome grief. Clinics, 2020; 75:e2206.

44.

Dzhurova

. Symbolic politics and government response to a national emergency: narrating the COVID-19 crisis. Administrative Theory & Praxis, 2020; 42:1–17.

45.

, Luan

, Li

, et al. Changing emotions in the COVID-19 pandemic: a four-wave longitudinal study in the United States and China. Social Science & Medicine, 2021; 285:114222.

46.

Pennebaker

, Francis

, Booth

. (2001) Linguistic Inquiry and Word Count: LIWC 2001. Mahway, NJ: Lawrence Erlbaum Associates, p. 71.

47.

Boyd

, Schwartz

. Natural language analysis and the psychology of verbal behavior: the past, present, and future states of the field. Journal of Language and Social Psychology, 2021; 40:21–41.

48.

Dudău

, Sava

. Performing multilingual analysis with Linguistic Inquiry and Word Count 2015 (LIWC2015). An equivalence study of four languages. Frontiers in Psychology, 2021; 12:2860.

49.

Pennebaker

, Boyd

, Jordan

, et al. (2015) The development and psychometric properties of LIWC2015. Austin, TX: University of Texas at Austin, p. 27.

50.

Wang

, Hernandez

, Newman

, et al. Twitter analysis: studying US weekly trends in work stress and emotion. Applied Psychology, 2016; 65:355–378.

51.

Lee

, Park

, Kang

, et al. (2020) Cross-lingual suicidal-oriented word embedding toward suicide prevention. In Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp. 2208–2217.

52.

Bahdanau

, Cho

, Bengio

. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:14090473. 2014.

53.

Gaur

, Kursuncu

, Alambo

, et al. (2018) “Let Me Tell You About Your Mental Health!” Contextualized classification of reddit posts to dsm-5 for web-based intervention. In CIKM ‘18: Proceedings of the 27th ACM International Conference on Information and Knowledge Management. New York, NY: Association for Computing Machinery, pp. 753–762.

54.

Mikolov

, Chen

, Corrado

, et al. Efficient estimation of word representations in vector space. arXiv preprint arXiv:13013781. 2013.

55.

Joulin

, Grave

, Bojanowski

, et al. Bag of tricks for efficient text classification. arXiv preprint arXiv:1607.01759 2016.

56.

Hochreiter

, Schmidhuber

. Long short-term memory. Neural Computation, 1997; 9:1735–1780.

57.

Gao

, Gudykunst

. Uncertainty, anxiety, and adaptation. International Journal of Intercultural Relations, 1990; 14:301–317.

58.

Hirsh

, Mar

, Peterson

. Psychological entropy: a framework for understanding uncertainty-related anxiety [in Croatian]. Psychological Review, 2012; 119:304–320.

59.

Rando

. Anticipatory grief: the term is a misnomer but the phenomenon exists. Journal of Palliative Care, 1988; 4:70–73.

60.

Kumar

, Nayar

. COVID 19 and its mental health consequences. 2021;, 30:1–2.

61.

Njiri

Mental Health and Covid-19: the Right to Mental Health during the COVID-19 Pandemic (May 12, 2020). Available at SSRN: https://ssrn.com/abstract=3598916 or https://dx-doi-org.web.bisu.edu.cn/10.2139/ssrn.3598916

62.

Usher

, Durkin

, Bhullar

. The COVID-19 pandemic and mental health impacts. International Journal of Mental Health Nursing, 2020; 29:315.

63.

Banati

, Jones

, Youssef

. Intersecting vulnerabilities: the impacts of COVID-19 on the psycho-emotional lives of young people in low-and middle-income countries. The European Journal of Development Research, 2020; 32:1613–1638.