COVID-19 pandemic lockdown: An emotional health perspective of Indians on Twitter

Chehal, Dimple; Gupta, Pratiksha; Gulati, Payal

doi:10.1177/0020764020940741

Cited by 38 publications

(20 citation statements)

References 6 publications

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“…However, in an online survey among mostly the residents of Hubei province, China average proportion of mental well-being was 49.4%, while in Germany 50% respondents reported anxiety and psychological distress [ 14 , 26 ]. In another online survey contemporary to the current study, mostly negative approach was reported during the later phase of lockdown [ 27 ]. Consistent with these observations, on overall calculation nearly 45% respondents were found to be at high risk of developing psychological distress, while more than half of the participants reported themselves as lonely and did not have high resilient coping ability.…”

Section: Discussionmentioning

confidence: 99%

Has loneliness and poor resilient coping influenced the magnitude of psychological distress among apparently healthy Indian adults during the lockdown? Evidence from a rapid online nation-wide cross-sectional survey

Lahiri

Jha²,

Acharya

et al. 2021

PLoS ONE

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Background The burden of psychological distress is increasing with the spread of the pandemic and also with the enforcement of its containment measures. The aim of this research was to determine the proportion of self-reported psychological distress, loneliness and degrees of resilient coping, and to also investigate the relationship of loneliness, coping and other variables with psychological distress among apparently healthy Indians during nation-wide lockdown period. Methods A cross-sectional, region-stratified survey using pre-designed pre-tested Google form disseminated via different social media platforms was conducted. A total of 1249 responses were analysed all over India. The form enquired about Socio-demographic profile, awareness on COVID pandemic and cases in the surroundings. UCLA Loneliness scale, Brief resilience and coping scale (BRCS) and Psychological distress scale (K6) assessed self-reported loneliness, coping and psychological distress, respectively. Special regressor technique adjusting for endogeneity and heteroskedasticity was used to extract the average marginal effects. Results Majority of the respondents were 18–35 years old, male, single and urban residents. News media, social media mostly acted as sources of information regarding COVID related news. Overall, 54.47% (95% CI: 51.39–57.53%) and 38.39% (95% CI: 35.57–41.29%) were reported to be lonely and had low resilient coping ability respectively. Around 44.68% had high risk of developing psychological distress. Being a student (average marginal effect coefficient (AMECoef).: -0.07, 95% CI: [-0.12, -0.01]) and perceiving lockdown as an effective measure (AMECoef: -0.11, 95% CI: [-0.19, -0.03]) were protective against psychological distress. Psychological distress was associated with male respondents (AMECoef 0.07, 95% CI: [0.02, 0.11]), low or medium resilient copers (AMECoef 0.89, 95% CI: [0.17, 1.61]), and perceiving a serious impact of social distancing measures (AMECoef 0.17, 95% CI: [0.09, 0.26]). Conclusions Psychological distress among Indian population during lockdown was prevalent. Poor coping ability and perceiving social distancing to have a serious impact was found to be significantly contributing to psychological distress. Appropriate measures to address these issues would be beneficial for the community mental health.

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Section: Discussionmentioning

confidence: 99%

Has loneliness and poor resilient coping influenced the magnitude of psychological distress among apparently healthy Indian adults during the lockdown? Evidence from a rapid online nation-wide cross-sectional survey

Lahiri

Jha²,

Acharya

et al. 2021

PLoS ONE

View full text Add to dashboard Cite

show abstract

“… Ref. Emotion Label Dataset Methodology adopted Lexicon/ Model used Advantages Limitations [32] Anger, Anxiety, Indignation, Negative emotion, Positive emotion Wiebo data pool Online Ecological Recognition (OER) LIWC2015 Lexicon For Policy improvement on mental health Case study on China from a social media platform [33] Anger, Anticipation, Disgust, Fear, Joy, Sadness, Surprise, Trust, positive, Negative Twitter dataset Lexicon-based approach NRC Word- Emotion Lexicon To understand the changing mindsets of people Only English tweets were collected for the study [35] Level of anxiety Online questionnaires ML-DL approach Hybrid Approach Understand psychological impact of epidemic on college students Online questionnaire [39] Anxiety, Fear, Sadness, Anger Questionnaire survey Lexical oriented approach. NRC Word-Emotion Association Lexicon To understand the coping strategies of nursing college students Online questionnaire [38] Willingness to self-isolate Online questionnaires Unsupervised learning Automated Model To help authorities understand public willingness to self-isolate Online questionnaire is used and study is restricted to U.S. only …”

Section: Related Workmentioning

confidence: 99%

“…The results also showed that the words of concern like "Health," "Family," and "Death" increased significantly, indicating that with passing the time, people were getting more concerned about their family and health. Another work [33] analyzed India's emotion during lockdown phase 2 and lockdown phase 3 on the Twitter dataset and analyzed the opinion on e-commerce during this pandemic. Their results showed a change in emotions during different lockdown phases.…”

Section: Related Workmentioning

confidence: 99%

An Emotion Care Model using Multimodal Textual Analysis on COVID-19

Gupta

Jain

Katariya

et al. 2021

Chaos, Solitons & Fractals

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“…Simultaneously, attention to group, religion, and emotions was more prevalent in Wuhan [ 18 ]. In India, a comparison between lockdown 2.0 (April 15 to May 3, 2020) and lockdown 3.0 (May 4 to May 17, 2020) revealed that the public's surprise, trust, fear, joy, and anger emotions decreased, but their anticipation, disgust, and sadness emotions increased [ 19 ]. However, policies such as physical distancing measures affected different emotions differently.…”

Section: Introductionmentioning

confidence: 99%

Temporal Dynamics of Public Emotions During the COVID-19 Pandemic at the Epicenter of the Outbreak: Sentiment Analysis of Weibo Posts From Wuhan

Yu¹,

Eisenman²,

Han³

2021

J Med Internet Res

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Background The ongoing COVID-19 pandemic has led to an increase in anxiety, depression, posttraumatic stress disorder, and psychological stress experienced by the general public in various degrees worldwide. However, effective, tailored mental health services and interventions cannot be achieved until we understand the patterns of mental health issues emerging after a public health crisis, especially in the context of the rapid transmission of COVID-19. Understanding the public's emotions and needs and their distribution attributes are therefore critical for creating appropriate public policies and eventually responding to the health crisis effectively, efficiently, and equitably. Objective This study aims to detect the temporal patterns in emotional fluctuation, significant events during the COVID-19 pandemic that affected emotional changes and variations, and hourly variations of emotions within a single day by analyzing data from the Chinese social media platform Weibo. Methods Based on a longitudinal dataset of 816,556 posts published by 27,912 Weibo users in Wuhan, China, from December 31, 2019, to April 31, 2020, we processed general sentiment inclination rating and the type of sentiments of Weibo posts by using pandas and SnowNLP Python libraries. We also grouped the publication times into 5 time groups to measure changes in netizens’ sentiments during different periods in a single day. Results Overall, negative emotions such as surprise, fear, and anger were the most salient emotions detected on Weibo. These emotions were triggered by certain milestone events such as the confirmation of human-to-human transmission of COVID-19. Emotions varied within a day. Although all emotions were more prevalent in the afternoon and night, fear and anger were more dominant in the morning and afternoon, whereas depression was more salient during the night. Conclusions Various milestone events during the COVID-19 pandemic were the primary events that ignited netizens’ emotions. In addition, Weibo users’ emotions varied within a day. Our findings provide insights into providing better-tailored mental health services and interventions.

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COVID-19 pandemic lockdown: An emotional health perspective of Indians on Twitter

Cited by 38 publications

References 6 publications

Has loneliness and poor resilient coping influenced the magnitude of psychological distress among apparently healthy Indian adults during the lockdown? Evidence from a rapid online nation-wide cross-sectional survey

Has loneliness and poor resilient coping influenced the magnitude of psychological distress among apparently healthy Indian adults during the lockdown? Evidence from a rapid online nation-wide cross-sectional survey

An Emotion Care Model using Multimodal Textual Analysis on COVID-19

Temporal Dynamics of Public Emotions During the COVID-19 Pandemic at the Epicenter of the Outbreak: Sentiment Analysis of Weibo Posts From Wuhan

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