Analysis of social media data for public emotion on the Wuhan lockdown event during the COVID-19 pandemic

Cao, Guang; Shen, Lining; Zhang, Zhiguo; Bi, Qiqing; Huang, Wenjing; Zhang, Wenli

doi:10.1016/j.cmpb.2021.106468

Cited by 22 publications

(15 citation statements)

References 41 publications

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“…A possible explanation for the discrepancies between our results and those of previous studies is the differences in word choice when coding the data. Studies that reported more negative emotions after the lockdown was imposed tended to use emotion types such as “stress,” “hostility,” “disappointment,” “surprise,” “fear,” “guilt,” and “blame” [ 2 , 4 , 25 , 28 , 32 ], and those that reported positive emotions after the lockdown was imposed tended to include emotion types such as “encouragement,” “admiration,” “hope,” and “blessedness” [ 2 , 28 , 32 ]. The word choices could have biased their results toward conclusions that were determined by the word categories chosen.…”

Section: Discussionmentioning

confidence: 99%

“…These posts may have contained emotion information. In the future, researchers could use machine learning and deep learning algorithms to capture the emotions embedded in the words that were not coded in the lexicon and map out emotional trajectories [ 2 , 32 ].…”

Section: Discussionmentioning

confidence: 99%

“…Using social media data, recent studies have found an increase in negative emotions such as fear, panic, guilt, anger, and disappointment [ 2 , 4 , 25 , 26 , 28 , 31 ] as well as an increase in positive emotions such as feeling blessedness, hope, admiration, and encouragement after the Wuhan lockdown [ 2 , 26 , 32 ]. It appears that Chinese people experienced increases in both pleasant and unpleasant emotions after the lockdown was imposed.…”

Section: Introductionmentioning

confidence: 99%

“…Su et al [ 28 ] and Yu et al [ 25 ] adopted “depression,” “like,” “dislike,” “anger,” “sadness,” “fear,” “enjoyment,” “disgust,” and “surprise” from the Affective Lexicon Ontology [ 33 ]. Shen et al [ 2 ] manually coded emotion words from corpora as “anger,” “fear,” “neutral,” “encouragement,” and “hope.” Cao et al [ 32 ] used the Ortony, Clore, and Collins model and the 6 emotions by Ekman to categorize emotions as “joy,” “hope,” “distress,” “fear,” “admiration,” “reproach,” and “neutral.” The limited emotion types used in previous studies might not be sufficient to construct a complete picture of emotional responses during the Wuhan lockdown. Applying a sentiment analysis based on the circumplex model of affect, this study sought to map the comprehensive emotion trajectories of Chinese people during the Wuhan lockdown.…”

Section: Introductionmentioning

confidence: 99%

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The Emotional Anatomy of the Wuhan Lockdown: Sentiment Analysis Using Weibo Data

Chen¹,

Yik²

2022

JMIR Form Res

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Background On January 23, 2020, the city of Wuhan, China, was sealed off in response to the COVID-19 pandemic. Studies have found that the lockdown was associated with both positive and negative emotions, although their findings are not conclusive. In these studies, emotional responses to the Wuhan lockdown were identified using lexicons based on limited emotion types. Objective This study aims to map Chinese people’s emotional responses to the Wuhan lockdown and compare Wuhan residents’ emotions with those of people elsewhere in China by analyzing social media data from Weibo using a lexicon based on the circumplex model of affect. Methods Social media posts on Weibo from 2 weeks before to 2 weeks after the Wuhan lockdown was imposed (January 9, 2020, to February 6, 2020) were collected. Each post was coded using a valence score and an arousal score. To map emotional trajectories during the study period, we used a data set of 359,190 posts. To compare the immediate emotional responses to the lockdown and its longer-term emotional impact on Wuhan residents (n=1236) and non-Hubei residents (n=12,714), we used a second data set of 57,685 posts for multilevel modeling analyses. Results Most posts (248,757/359,190, 69.25%) made during the studied lockdown period indicated a pleasant mood with low arousal. A gradual increase in both valence and arousal before the lockdown was observed. The posts after the lockdown was imposed had higher valence and arousal than prelockdown posts. On the day of lockdown, the non-Hubei group had a temporarily boosted valence (γ20=0.118; SE 0.021; P<.001) and arousal (γ30=0.293; SE 0.022; P<.001). Compared with non-Hubei residents, the Wuhan group had smaller increases in valence (γ21=−0.172; SE 0.052; P<.001) and arousal (γ31=−0.262; SE 0.053; P<.001) on the day of lockdown. Weibo users’ emotional valence (γ40=0.000; SE 0.001; P=.71) and arousal (γ40=0.001; SE 0.001; P=.56) remained stable over the 2 weeks after the lockdown was imposed regardless of geographical location (valence: γ41=−0.004, SE 0.003, and P=.16; arousal: γ41=0.003, SE 0.003, and P=.26). Conclusions During the early stages of the pandemic, most Weibo posts indicated a pleasant mood with low arousal. The overall increase in the posts’ valence and arousal after the lockdown announcement might indicate collective cohesion and mutual support in web-based communities during a public health crisis. Compared with the temporary increases in valence and arousal of non-Hubei users on the day of lockdown, Wuhan residents’ emotions were less affected by the announcement. Overall, our data suggest that Weibo users were not influenced by the lockdown measures in the 2 weeks after the lockdown announcement. Our findings offer policy makers insights into the usefulness of social connections in maintaining the psychological well-being of people affected by a lockdown.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Emotional Anatomy of the Wuhan Lockdown: Sentiment Analysis Using Weibo Data

Chen¹,

Yik²

2022

JMIR Form Res

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“…Across four forums, they found that words or phrases that are related to subjective mortality salience (e.g., "cemetery, " "survive, " "death"), engagement in subsistence activities (e.g., "farm, " "garden, " "cook"), and collectivism showed increases (e.g., "sacrifice, " "share, " "help"), suggesting that human may shift their behavior according to the level of death and availability of resources. Cao et al (2021) leveraged Latent Dirichlet Allocation (LDA) (Blei et al, 2003) to mine the various topics in the Sina Weibo posts that show attitudes toward the lockdown policy in Wuhan. LDA is a generative probabilistic model of a corpus.…”

Section: Content Analysismentioning

confidence: 99%

Human behavior in the time of COVID-19: Learning from big data

et al. 2023

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Since the World Health Organization (WHO) characterized COVID-19 as a pandemic in March 2020, there have been over 600 million confirmed cases of COVID-19 and more than six million deaths as of October 2022. The relationship between the COVID-19 pandemic and human behavior is complicated. On one hand, human behavior is found to shape the spread of the disease. On the other hand, the pandemic has impacted and even changed human behavior in almost every aspect. To provide a holistic understanding of the complex interplay between human behavior and the COVID-19 pandemic, researchers have been employing big data techniques such as natural language processing, computer vision, audio signal processing, frequent pattern mining, and machine learning. In this study, we present an overview of the existing studies on using big data techniques to study human behavior in the time of the COVID-19 pandemic. In particular, we categorize these studies into three groups—using big data to measure, model, and leverage human behavior, respectively. The related tasks, data, and methods are summarized accordingly. To provide more insights into how to fight the COVID-19 pandemic and future global catastrophes, we further discuss challenges and potential opportunities.

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Digitales Contact Tracing als Lösungsoption während der COVID-19-Pandemie? – Datenschutz vs. Datenschatz

Fischer

2023

Medizin, Kultur, Gesellschaft

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Analysis of social media data for public emotion on the Wuhan lockdown event during the COVID-19 pandemic

Cited by 22 publications

References 41 publications

The Emotional Anatomy of the Wuhan Lockdown: Sentiment Analysis Using Weibo Data

The Emotional Anatomy of the Wuhan Lockdown: Sentiment Analysis Using Weibo Data

Human behavior in the time of COVID-19: Learning from big data

Digitales Contact Tracing als Lösungsoption während der COVID-19-Pandemie? – Datenschutz vs. Datenschatz

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