Improving Google Flu Trends for COVID-19 estimates using Weibo posts

Guo, Shuhui; Fang, Fan; Zhou, Tao; Zhang, Wei; Guo, Qiang; Zeng, Rui; Chen, Xiaohong; Liu, Jianguo; Lu, Xin

doi:10.1016/j.dsm.2021.07.001

Cited by 15 publications

(8 citation statements)

References 47 publications

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“…In research related to the COVID-19 vaccine, using social media data for academic research has become an emerging trend. Social media provides a rich volume of real-time and cost-effective content including news, events, public comments, etc., [ 5 ], which has been widely used in health-related issues and public health crises [ 6 , 7 , 8 , 9 ]. However, research on the COVID-19 vaccine mainly employs the classic time series analysis based on discrete observation data.…”

Section: Introductionmentioning

confidence: 99%

Revealing Public Opinion towards the COVID-19 Vaccine with Weibo Data in China: BertFDA-Based Model

Zhu

Weng

Zhuang

et al. 2022

IJERPH

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The COVID-19 pandemic has created unprecedented burdens on people’s health and subjective well-being. While countries around the world have established models to track and predict the affective states of COVID-19, identifying the topics of public discussion and sentiment evolution of the vaccine, particularly the differences in topics of concern between vaccine-support and vaccine-hesitant groups, remains scarce. Using social media data from the two years following the outbreak of COVID-19 (23 January 2020 to 23 January 2022), coupled with state-of-the-art natural language processing (NLP) techniques, we developed a public opinion analysis framework (BertFDA). First, using dynamic topic clustering on Weibo through the latent Dirichlet allocation (LDA) model, a total of 118 topics were generated in 24 months using 2,211,806 microblog posts. Second, by building an improved Bert pre-training model for sentiment classification, we provide evidence that public negative sentiment continued to decline in the early stages of COVID-19 vaccination. Third, by modeling and analyzing the microblog posts from the vaccine-support group and the vaccine-hesitant group, we discover that the vaccine-support group was more concerned about vaccine effectiveness and the reporting of news, reflecting greater group cohesion, whereas the vaccine-hesitant group was particularly concerned about the spread of coronavirus variants and vaccine side effects. Finally, we deployed different machine learning models to predict public opinion. Moreover, functional data analysis (FDA) is developed to build the functional sentiment curve, which can effectively capture the dynamic changes with the explicit function. This study can aid governments in developing effective interventions and education campaigns to boost vaccination rates.

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Section: Introductionmentioning

confidence: 99%

Revealing Public Opinion towards the COVID-19 Vaccine with Weibo Data in China: BertFDA-Based Model

Zhu

Weng

Zhuang

et al. 2022

IJERPH

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“…Next, we calculated the number of microblogs containing keywords from the three categories. From Figure 1 , we can see that Basic awareness is the type with the largest number of microblogs related to the epidemic, which indicates most people's concern primarily focus on Basic awareness rather than some awareness deeper like “mask” and “social distance.” Also, the changes in the number of various microblogs have a similar trend with the increase of confirmed cases and a particular role in early warning ( 41 , 42 ).…”

Section: Resultsmentioning

confidence: 78%

Network Structure and Community Evolution Online: Behavioral and Emotional Changes in Response to COVID-19

Fang

Wang

Tan

et al. 2022

Front. Public Health

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Background: The measurement and identification of changes in the social structure in response to an exceptional event like COVID-19 can facilitate a more informed public response to the pandemic and provide fundamental insights on how collective social processes respond to extreme events.Objective: In this study, we built a generalized framework for applying social media data to understand public behavioral and emotional changes in response to COVID-19.Methods: Utilizing a complete dataset of Sina Weibo posts published by users in Wuhan from December 2019 to March 2020, we constructed a time-varying social network of 3.5 million users. In combination with community detection, text analysis, and sentiment analysis, we comprehensively analyzed the evolution of the social network structure, as well as the behavioral and emotional changes across four main stages of Wuhan's experience with the epidemic.Results: The empirical results indicate that almost all network indicators related to the network's size and the frequency of social interactions increased during the outbreak. The number of unique recipients, average degree, and transitivity increased by 24, 23, and 19% during the severe stage than before the outbreak, respectively. Additionally, the similarity of topics discussed on Weibo increased during the local peak of the epidemic. Most people began discussing the epidemic instead of the more varied cultural topics that dominated early conversations. The number of communities focused on COVID-19 increased by nearly 40 percent of the total number of communities. Finally, we find a statistically significant “rebound effect” by exploring the emotional content of the users' posts through paired sample t-test (P = 0.003).Conclusions: Following the evolution of the network and community structure can explain how collective social processes changed during the pandemic. These results can provide data-driven insights into the development of public attention during extreme events.

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“…The unprecedented magnitude and transmission speed of COVID-19 has intensified massive social media activities as people are isolated at home to break infection chains [ 21 – 26 ]. The large amount of social data generated by online users can help us understand the topics of concern and emotional fluctuations of the epidemic-related population in real time, which is necessary for timely detection of people’s needs and emotional counselling.…”

Section: Related Workmentioning

confidence: 99%

Emoji use in China: popularity patterns and changes due to COVID-19

et al. 2022

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Emojis are small pictograms that are frequently embedded within micro-texts to more directly express emotional meanings. To understand the changes in the emoji usage of internet users during the COVID-19 outbreak, we analysed a large dataset collected from Weibo, the most popular Twitter-like social media platform in China, from December 1, 2019, to March 20, 2020. The data contained 38,183,194 microblog posts published by 2,239,472 unique users in Wuhan. We calculated the basic statistics of users’ usage of emojis, topics, and sentiments and analysed the temporal patterns of emoji occurrence. After examining the emoji co-occurrence structure, we finally explored other factors that may affect individual emoji usage. We found that the COVID-19 outbreak greatly changed the pattern of emoji usage; i.e., both the proportion of posts containing emojis and the ratio of users using emojis declined substantially, while the number of posts remained the same. The daily proportion of Happy emojis significantly declined to approximately 32%, but the proportions of Sad- and Encouraging-related emojis rose to 24% and 34%, respectively. Despite a significant decrease in the number of nodes and edges in the emoji co-occurrence network, the average degree of the network increased from 34 to 39.8, indicating that the diversity of emoji usage increased. Most interestingly, we found that male users were more inclined towards using regular textual language with fewer emojis after the pandemic, suggesting that during public crises, male groups appeared to control their emotional display. In summary, the COVID-19 pandemic remarkably impacted individual sentiments, and the normal pattern of emoji usage tends to change significantly following a public emergency. Supplementary Information The online version contains supplementary material available at 10.1007/s10489-022-03195-y.

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Improving Google Flu Trends for COVID-19 estimates using Weibo posts

Cited by 15 publications

References 47 publications

Revealing Public Opinion towards the COVID-19 Vaccine with Weibo Data in China: BertFDA-Based Model

Revealing Public Opinion towards the COVID-19 Vaccine with Weibo Data in China: BertFDA-Based Model

Network Structure and Community Evolution Online: Behavioral and Emotional Changes in Response to COVID-19

Emoji use in China: popularity patterns and changes due to COVID-19

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