Application of the Internet Platform in Monitoring Chinese Public Attention to the Outbreak of COVID-19

Gong, Xue; Hou, Mengchi; Han, Yangyang; Liang, Hailun; Guo, Ruifeng

doi:10.3389/fpubh.2021.755530

Cited by 6 publications

(6 citation statements)

References 31 publications

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“…The volume of the Baidu Information Index of each keyword was much higher than that of the Baidu Search Index and had 0 to 14 days' lag time to the number of new reported cases. This finding was inconsistent with another study that showed Sina Micro Index had advance effects on the COVID-19 outbreak 16 . This could be explained as Baidu Information Index weighted sum of and indexed the netizen's reading, comments, forwarding, likes, dislikes, and other behaviors.…”

Section: Discussioncontrasting

confidence: 77%

“…This finding was inconsistent with another study that showed Sina Micro Index had advance effects on the COVID-19 outbreak. 16 This could be explained as Baidu Information Index weighted sum of and indexed the netizen's reading, comments, forwarding, likes, dislikes, and other behaviors. As supply-based data, it is easily driven by hot events, which can arouse public attention and stimulate public search behavior to a certain topic.…”

Section: Discussionmentioning

confidence: 99%

“…The supply-based data were also important for monitoring people's status updates on blogs and microblogs for outbreak detection. 15 , 16 But studies focusing on both demand- and supply-based data are limited. We investigated the predictive utility of Baidu Search Index and Baidu Information Index for early warning of the COVID-19 outbreak and for further monitoring of the epidemic trends in Guangxi and identification of the related keywords.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Early Warning and Monitoring of Coronavirus Disease 2019 Using Baidu Search Index and Baidu Information Index in Guangxi, China

Xie¹,

Zhou²,

Zhu³

et al. 2022

Infect Microb Dis

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Coronavirus disease 2019 (COVID-19) is an emerging infectious disease, and it is important to detect early and monitor the disease trend for policymakers to make informed decisions. We explored the predictive utility of Baidu Search Index and Baidu Information Index for early warning of COVID-19 and identified search keywords for further monitoring of epidemic trends in Guangxi. A time-series analysis and Spearman correlation between the daily number of cases and both the Baidu Search Index and Baidu Information Index were performed for seven keywords related to COVID-19 from January 8 to March 9, 2020. The time series showed that the temporal distributions of the search terms “coronavirus,” “pneumonia” and “mask” in the Baidu Search Index were consistent and had 2 to 3 days' lead time to the reported cases; the correlation coefficients were higher than 0.81. The Baidu Search Index volume in 14 prefectures of Guangxi was closely related with the number of reported cases; it was not associated with the local GDP. The Baidu Information Index search terms “coronavirus” and “pneumonia” were used as frequently as 192,405.0 and 110,488.6 per million population, respectively, and they were also significantly associated with the number of reported cases (rs > 0.6), but they fluctuated more than for the Baidu Search Index and had 0 to 14 days' lag time to the reported cases. The Baidu Search Index with search terms “coronavirus,” “pneumonia” and “mask” can be used for early warning and monitoring of the epidemic trend of COVID-19 in Guangxi, with 2 to 3 days' lead time.

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Section: Discussioncontrasting

confidence: 77%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Early Warning and Monitoring of Coronavirus Disease 2019 Using Baidu Search Index and Baidu Information Index in Guangxi, China

Xie¹,

Zhou²,

Zhu³

et al. 2022

Infect Microb Dis

View full text Add to dashboard Cite

show abstract

“…On this basis, it is feasible to categorize the stages of change in public attention by keeping track of variations in the numbers of hot searches relating to the pandemic during the study period. Given the limitations of single-source hot search samples, this paper also used another common index of the public attention to certain events, the Baidu index [ 51 , 52 , 53 ], to corroborate the classification results. Jieba word classification database tools were used to process the content of the hot search entries, with “novel coronavirus”, “pandemic”, “case”, “pneumonia”, and “new pneumonia” being identified as the top five keywords.…”

Section: Methodsmentioning

confidence: 99%

Characteristics of Collective Resilience and Its Influencing Factors from the Perspective of Psychological Emotion: A Case Study of COVID-19 in China

Liu

et al. 2022

IJERPH

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Collective resilience is the ability of human beings to adapt and collectively cope with crises in adversity. Emotional expression is the core element with which to characterize the psychological dimension of collective resilience. This research proposed a stage model of collective resilience based on the temporal evolution of the public opinions of COVID-19 in China’s first anti-pandemic cycle; using data from hot searches and commentaries on Sina Weibo, the changes in the emotional patterns of social groups are revealed through analyses of the sentiments expressed in texts. A grounded theory approach is used to elucidate the factors influencing collective resilience. The research results show that collective resilience during the pandemic exhibited an evolutionary process that could be termed, “preparation–process–recovery”. Analyses of expressed sentiments reveal an evolutionary pattern of “positive emotion prevailing–negative emotion appearing–positive emotion recovering Collective resilience from a psycho-emotional perspective is the result of “basic cognition-intermediary condition-consequence” positive feedback, in which the basic cognition is expressed as will embeddedness and the intermediary conditions include the subject behavior and any associated derived behavioral characteristics and spiritual connotation. These results are significant both theoretically and practically with regard to the reconstruction of collective resilience when s‘ force majeure’ event occur.

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“…Therefore, Baidu Index is reliable. Infodemiology studies based on the Baidu Index platform mainly involve the application in COVID-19 prediction and monitoring ( 60 , 61 ) but have not investigated OAV. In terms of research content, few studies have analyzed the spatial characteristics of OAV and their causes.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal evolution of online attention to vaccines since 2011: An empirical study in China

Qiu

Xia

et al. 2022

Front. Public Health

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Since the outbreak of Coronavirus Disease 2019 (COVID-19), the Chinese government has taken a number of measures to effectively control the pandemic. By the end of 2021, China achieved a full vaccination rate higher than 85%. The Chinese Plan provides an important model for the global fight against COVID-19. Internet search reflects the public's attention toward and potential demand for a particular thing. Research on the spatiotemporal characteristics of online attention to vaccines can determine the spatiotemporal distribution of vaccine demand in China and provides a basis for global public health policy making. This study analyzes the spatiotemporal characteristics of online attention to vaccines and their influencing factors in 31 provinces/municipalities in mainland China with Baidu Index as the data source by using geographic concentration index, coefficient of variation, GeoDetector, and other methods. The following findings are presented. First, online attention to vaccines showed an overall upward trend in China since 2011, especially after 2016. Significant seasonal differences and an unbalanced monthly distribution were observed. Second, there was an obvious geographical imbalance in online attention to vaccines among the provinces/municipalities, generally exhibiting a spatial pattern of “high in the east and low in the west.” Low aggregation and obvious spatial dispersion among the provinces/municipalities were also observed. The geographic distribution of hot and cold spots of online attention to vaccines has clear boundaries. The hot spots are mainly distributed in the central-eastern provinces and the cold spots are in the western provinces. Third, the spatiotemporal differences in online attention to vaccines are the combined result of socioeconomic level, socio-demographic characteristics, and disease control level.

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Application of the Internet Platform in Monitoring Chinese Public Attention to the Outbreak of COVID-19

Cited by 6 publications

References 31 publications

Early Warning and Monitoring of Coronavirus Disease 2019 Using Baidu Search Index and Baidu Information Index in Guangxi, China

Early Warning and Monitoring of Coronavirus Disease 2019 Using Baidu Search Index and Baidu Information Index in Guangxi, China

Characteristics of Collective Resilience and Its Influencing Factors from the Perspective of Psychological Emotion: A Case Study of COVID-19 in China

Spatiotemporal evolution of online attention to vaccines since 2011: An empirical study in China

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