Analysis of spatiotemporal characteristics of big data on social media sentiment with COVID-19 epidemic topics

Zhu, Bangren; Zheng, Xinqi; Liu, Haiyan; Li, Jiayang; Wang, Peipei

doi:10.1016/j.chaos.2020.110123

Cited by 114 publications

(74 citation statements)

References 31 publications

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“…All data regarding COVID-19 posts were retrieved, and 723,216 posts were extracted in total. To improve the representativeness of data, we removed incomplete data from the fields and deleted text longer than 400 Chinese characters [ 41 ], thus obtaining data from a total of 143,197 posts. Because most of these posts were reposts, we only retained 19,188 of the original post data.…”

Section: Methodsmentioning

confidence: 99%

Characterizing the COVID-19 Infodemic on Chinese Social Media: Exploratory Study

Zhang¹,

Pian²,

Ma³

et al. 2021

JMIR Public Health Surveill

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Background The COVID-19 infodemic has been disseminating rapidly on social media and posing a significant threat to people’s health and governance systems. Objective This study aimed to investigate and analyze posts related to COVID-19 misinformation on major Chinese social media platforms in order to characterize the COVID-19 infodemic. Methods We collected posts related to COVID-19 misinformation published on major Chinese social media platforms from January 20 to May 28, 2020, by using PythonToolkit. We used content analysis to identify the quantity and source of prevalent posts and topic modeling to cluster themes related to the COVID-19 infodemic. Furthermore, we explored the quantity, sources, and theme characteristics of the COVID-19 infodemic over time. Results The daily number of social media posts related to the COVID-19 infodemic was positively correlated with the daily number of newly confirmed (r=0.672, P<.01) and newly suspected (r=0.497, P<.01) COVID-19 cases. The COVID-19 infodemic showed a characteristic of gradual progress, which can be divided into 5 stages: incubation, outbreak, stalemate, control, and recovery. The sources of the COVID-19 infodemic can be divided into 5 types: chat platforms (1100/2745, 40.07%), video-sharing platforms (642/2745, 23.39%), news-sharing platforms (607/2745, 22.11%), health care platforms (239/2745, 8.71%), and Q&A platforms (157/2745, 5.72%), which slightly differed at each stage. The themes related to the COVID-19 infodemic were clustered into 8 categories: “conspiracy theories” (648/2745, 23.61%), “government response” (544/2745, 19.82%), “prevention action” (411/2745, 14.97%), “new cases” (365/2745, 13.30%), “transmission routes” (244/2745, 8.89%), “origin and nomenclature” (228/2745, 8.30%), “vaccines and medicines” (154/2745, 5.61%), and “symptoms and detection” (151/2745, 5.50%), which were prominently diverse at different stages. Additionally, the COVID-19 infodemic showed the characteristic of repeated fluctuations. Conclusions Our study found that the COVID-19 infodemic on Chinese social media was characterized by gradual progress, videoization, and repeated fluctuations. Furthermore, our findings suggest that the COVID-19 infodemic is paralleled to the propagation of the COVID-19 epidemic. We have tracked the COVID-19 infodemic across Chinese social media, providing critical new insights into the characteristics of the infodemic and pointing out opportunities for preventing and controlling the COVID-19 infodemic.

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Section: Methodsmentioning

confidence: 99%

Characterizing the COVID-19 Infodemic on Chinese Social Media: Exploratory Study

Zhang¹,

Pian²,

Ma³

et al. 2021

JMIR Public Health Surveill

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“…Despite the advancement and development of medical science, COVID-19 is the most perilous disease of the 21st century around the world, which is a critical threat to the physical and mental health of individuals. In this respect, Zhu, et al ( Zhu et al, 2020 ) analysed the topics about COVID-19 in Weibo from January 24 to February 25, 2020. The authors tried to grasp the opinions of users about the epidemic from a temporal and spatial perspective in China.…”

Section: Classification Of the Selected Papersmentioning

confidence: 99%

Big data analytics meets social media: A systematic review of techniques, open issues, and future directions

Abkenar

Kashani

Mahdipour

et al. 2021

Telematics and Informatics

112

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“…During the early phase of the outbreak, before March 15, 2020, the main topics discussed on Twitter in the English language were the origin of the virus, its sources, transmission, the impact on the economy and society, and the methods of mitigating the risks [ 12 ]. Similarly, on the Chinese social media platform Weibo, the primary concerns among users were the origin of the disease, public health control measures, disease symptoms, organizations in charge, health professionals and scientists, education sectors, economic impact, and rumor discussions [ 13 , 14 ]. Since the COVID-19 has lasted for such a long time and is still an ongoing pandemic, the public’s concerns and emotional responses have evolved with the pandemic's rapid change, its impacts, and countermeasure policies [ 15 ].…”

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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Analysis of spatiotemporal characteristics of big data on social media sentiment with COVID-19 epidemic topics

Abstract: Highlights Discover the new characteristics of the “double peaks” of public opinion. Popular topics have the characteristic of slowly declining over time. There is no inevitable temporal and spatial consistency between topics of high concern and topics of low emotion.

Cited by 114 publications

References 31 publications

Characterizing the COVID-19 Infodemic on Chinese Social Media: Exploratory Study

Characterizing the COVID-19 Infodemic on Chinese Social Media: Exploratory Study

Big data analytics meets social media: A systematic review of techniques, open issues, and future directions

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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