Public Opinion Analysis for the Covid-19 Pandemic Based on Sina Weibo Data

The outbreak of Covid-19 has been continuously affecting human lives and communities around the world in many ways. In order to effectively prevent and control the Covid-19 pandemic, public opinion is analyzed based on Sina Weibo data in this paper. Firstly the Weibo data was crawled from Sina website to be the experimental dataset. After preprocessing operations of data cleaning, word segmentation and stop words removal, Term Frequency Inverse Document Frequency (TF-IDF) method was used to perform feature extraction and vectorization. Then public opinion for the Covid-19 pandemic was analyzed, which included word cloud analysis based on text visualization, topic mining based on Latent Dirichlet Allocation (LDA) and sentiment analysis based on Naïve Bayes. The experimental results show that public opinion analysis based on Sina Weibo data can provide effective data support for prevention and control of the Covid-19 pandemic. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

How health organizations communicate about COVID-19 on social media: a comparative content analysis.

BACKGROUND: This study examined how different health organizations (i.e., the Chinese CDC, the Korean CDC, the United States CDC, and WHO) communicated about the COVID-19 pandemic on social media, thus providing implications for organizations touse social media effectively in global health crises in the future. METHODS: Three bilingual researchers conducted a content analysis ofsocial media posts (N = 1,343) of these health organizations on Twitter and Sina Weibo to explore the frames of the COVID-19 pandemic, the purposes, and the strategies to communicate about it. RESULTS: Prevention was the dominant frame of the social media content of these four health organizations. Information update was the major communication purpose for WHO, the United States CDC, and the Korean CDC; however, guidance was the primary communication purpose for the Chinese CDC. The United States CDC, the Chinese CDC, and the Korean CDC heavily relied on multiple social media strategies (i.e., visual, hyperlink, and authority quotation) in their communication to the public about the COVID-19 pandemic, whereas WHO primarily employed quoting authorities. Significantdifferences were revealed across these health organizations in frames, communication purposes, and strategies. Theoretical and practical implications and limitations were discussed. CONCLUSIONS: This study examined how different global health organizations communicate about the COVID-19 pandemic on social media. We discussed how and why these global health organizations communicate the COVID-19 pandemic, which would help health-related organizations design messages strategically on global public health issues in the future.

China public emotion analysis under normalization of COVID-19 epidemic: Using Sina Weibo.

The prevention and control of the coronavirus disease 2019 (COVID-19) epidemic in China has entered a phase of normalization. The basis for evaluating and improving public health strategies is understanding the emotions and concerns of the public. This study establishes a fine-grained emotion-classification model to annotate the emotions of 32,698 Sina Weibo posts related to COVID-19 prevention and control from July 2022 to August 2022. The Dalian University of Technology (DLUT) emotion-classification system was adjusted to form four pairs (eight categories) of bidirectional emotions: good-disgust, joy-sadness, anger-fear, and surprise-anticipation. A lexicon-based method was proposed to classify the emotions of Weibo posts. Based on the selected Weibo posts, the present study analyzed the Chinese public's sentiments and emotions. The results showed that positive sentiment accounted for 51%, negative sentiment accounted for 24%, and neutral sentiment accounted for 25%. Positive sentiments were dominated by good and joy emotions, and negative sentiments were dominated by fear and disgust emotions. The proportion of positive sentiments on official Weibo (accounts belonging to government departments and official media) is significantly higher than that on personal Weibo. Official Weibo users displayed a weak guiding effect on personal users in terms of positive sentiment and the two groups of users were almost completely synchronized in terms of negative sentiment. The linear discriminant analysis (LDA) was performed on the two negative emotions of fear and disgust in the personal posts. The present study found that the emotion of fear was mainly related to COVID-19 infection and death, control of people with positive nucleic acid tests, and the outbreak of local epidemic, while the emotion of disgust was mainly related to the long-term existence of the epidemic, the cost of nucleic acid tests, non-implementation of prevention and control measures, and the occurrence of foreign epidemics. These findings suggest that Chinese attitudes toward epidemic prevention and control are positive and optimistic; however, there is also a notable proportion of fear and disgust. It is expected that this study will help public health administrators to evaluate the effectiveness of possible countermeasures and work toward precise prevention and control of the COVID-19 epidemic.

SARS-CoV-2 outbreak uplifted collectivistic expression in the Hubei Daily

Following the outbreak of SARS-CoV-2, collectivistic words have been used more frequently on Sina Weibo and the People's Daily. However, the studies on Sina Weibo and the People's Daily can only reflect the overall impact by SARS-CoV-2 in China. To examine the influence of SARS-CoV-2 on collectivism/individualism, we first investigated the Hubei Daily, an authoritative local media in Hubei, the first province to discover SARS-CoV-2, to see the impact of SARS-CoV-2 on collectivism/individualism. We analyzed data from the Hubei Daily, using the same collectivistic/individualistic words identified in prior studies and found that the outbreak of SARS-CoV-2 increased collectivism and decreased individualism significantly. Next, we analyzed the same data using the individualistic/collectivistic word bank created in a different cross-culture study based on Sina Weibo posts. The results showed that, during the SARS-CoV-2, collectivistic words were used more frequently;no significant changes were seen regarding individualist words. Lastly, we created a COVID-19 word bank and conducted a regression analysis to examine the relationship between collectivistic word frequency and COVID-19 word frequency in the after-breakout period of SARS-CoV-2 and found that the severity of SARS-CoV-2 predicted collectivist word frequency change in the Hubei Daily. © 2022 IEEE.

Identification and Analysis of Opinion Leaders for COVID-19 Related Rumor Spreading in Social Media

Since the outbreak of COVID-19, thousands of rumors have occurred on social media, and it is significant to identify opinion leaders who play decisive roles during rumor spreading. However, existing literature lacks such opinion leaders identification and following analysis of COVID-19 background. So this paper takes a COVID-19 case as an example and collects data from Sina Weibo, which is a popular twitter-like social media in China. Then three different centrality measures are applied. Finally, a venn diagram is used to analyze opinion leaders identified, and profiles of them on Weibo are taken into consideration. In conclusion, the paper finds that opinion leaders identified during rumor spreading are institutional and individual accounts with a huge number of followers. But in terms of numbers, government institutions spread information to more people;in terms of breadth, impactful individual accounts deliver more information to more people from all walks of life. © VDE VERLAG GMBH - Berlin - Offenbach.

Data Mining and Spatial Analysis of Social Media Text Based on the BERT-CNN Model to Achieve Situational Awareness: a Case Study of COVID-19

In response to the COVID-19, social media big data has played an important role in epidemic warning, tracking the source of infection, and public opinion monitoring, providing strong technical support for China’s epidemic prevention and control work. The paper used Sina Weibo posts related to COVID-19 hashtags as the data source, and built a BERT-CNN deep learning model to perform fine-grained and high-precision topic classificationon massive social media posts. Taking Shenzhen as a region of interest, we mined the “epidemic data bulletin” and “daily life impact” posts during the epidemic for spatial analysis. The results show that the confirmed communities and designated hospitals in Shenzhen as a whole present the characteristics of “sparse east and dense west”, and there is a strong positive spatial correlation between the number of confirmed cases and social media response. Specifically, Nanshan District, Futian District and Luohu District have more confirmed cases due to large population movements and dense transportation networks, and social media has responded more violently, and people’s lives have been greatly affected. However, Yantian District, Pingshan District and Dapeng New District showed opposite characteristics. The case study results further show that using deep learning methods to mine text information in social media is scientifically feasible for improving situational awareness and decision support during the COVID-19.

An Analysis on the Weibo Topic Detection Based on K-means Algorithm

Sina Weibo, as a platform for netizens to express their opinions, generates a large amount of public opinion data and constantly generates new topics. How to detect new and hot topics on Weibo is a meaningful studied issue. Document Clustering is a widely studied problem in Text Categorization. K-means is one of the most famous unsupervised learning algorithms, partitions a given dataset into disjoint clusters following a simple and easy way. But the traditional K-means algorithm assigns initial centroids randomly, which cannot guarantee to choose the maximum dissimilar documents as the centroids for the clusters. A modified K-means algorithm is proposed, which uses Jaccard distance measure for assigning the most dissimilar k documents as centroids, and uses Word2vec as the Chinese text vectorization model. The experimental results demonstrate that the proposed K-means algorithm improves the clustering performance, and is able to detect new and hot topics based on Weibo COVID-19 data. © 2022 IEEE.

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

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.

Lift the veil of rumors: the impact of the characteristics of information sources on the effectiveness of rumors spreading

PurposeThis paper aims to investigate the impacts of rumors' information characteristics on people's believing and spreading of rumors online.Design/methodology/approachThis study employed a mixed-methods approach by combining qualitative and quantitative methods. In study 1, the authors explored different types of rumors and their information source characteristics through qualitative research. In study 2, the authors utilized the findings from study 1 to develop an empirical model to verify the impact of these characteristics on the public's behaviors of believing and spreading rumors by content analysis and quantitative research.FindingsThe results show that five information source characteristics – credibility, professionalism, attractiveness, mystery and concreteness – influence the spreading effect of different types of rumors.Research limitations/implicationsThis study contributes to rumor spreading research by deepening the theory of information source characteristics and adding to the emerging literature on the COVID-19 pandemic.Practical implicationsInsights from this research offer important practical implications for policymakers and online-platform operators by highlighting how to suppress the spread of rumors, particularly those associated with COVID-19.Originality/valueThis research introduces the theory of information source characteristics into the field of rumor spreading and adopts a mixed-methods approach, taking COVID-19 rumors as a typical case, which provides a unique perspective for a deeper understanding of rumor spreading's antecedences.

Local COVID-19 Severity and Social Media Responses: Evidence From China.

Unexpected but exceedingly consequential, the COVID-19 outbreak has undermined livelihoods, disrupted the economy, induced upheavals, and posed challenges to government decision-makers. Under various behavioural regulations, such as social distancing and transport limitations, social media has become the central platform on which people from all regions, regardless of local COVID-19 severity, share their feelings and exchange thoughts. Our study illustrates the evolution of moods expressed on social media regarding COVID-19-related issues and empirically confirms the hypothesis that the severity of the pandemic substantially correlates with these sentiments by analysing tweets on Sina Weibo (China's central social media platform). Methodologically, we leveraged Sentiment Knowledge Enhanced Pre-training, the most state-of-the-art natural language processing pre-trained sentiment-related multipurpose model, to label Sina Weibo tweets during the most distressed period in 2020. Given that the model itself does not provide a feature explanation, we utilize a random forest and linear probit model with the labelled sample to demonstrate how each word plays a role in the prediction. Finally, we demonstrate a strong negative linear relationship between the local severity of COVID-19 and the local sentiment response by incorporating miscellaneous geo-economic control variables. In short, our study reveals how pandemics affect local sentiment and, in a broader sense, provides an easy-to-implement and explanatory pipeline to classify sentiments and resolve related socioeconomic issues.

China Public Psychology Analysis About COVID-19 Under Considering Sina Weibo Data.

COVID-19 not only poses a huge threat to public health, but also affects people's mental health. Take scientific and effective psychological crisis intervention to prevent large-scale negative emotional contagion is an important task for epidemic prevention and control. This paper established a sentiment classification model to make sentiment annotation (positive and negative) about the 105,536 epidemic comments in 86 days on the official Weibo of People's Daily, the test results showed that the accuracy of the model reached 88%, and the AUC value was greater than 0.9. Based on the marked data set, we explored the potential law between the changes in Internet public opinion and epidemic situation in China. First of all, we found that most of the Weibo users showed positive emotions, and the negative emotions were mainly caused by the fear and concern about the epidemic itself and the doubts about the work of the government. Secondly, there is a strong correlation between the changes of epidemic situation and people's emotion. Also, we divided the epidemic into three period. The proportion of people's negative emotions showed a similar trend with the number of newly confirmed cases in the growth and decay period, and the extinction period. In addition, we also found that women have more positive emotional performance than men, and the high-impact groups is also more positive than the low-impact groups. We hope that these conclusions can help China and other countries experiencing severe epidemics to guide publics respond.

Longitudinal Change of Mental Health among Active Social Media Users in China during the COVID-19 Outbreak.

During the COVID-19 pandemic, every day, updated case numbers and the lasting time of the pandemic became major concerns of people. We collected the online data (28 January to 7 March 2020 during the COVID-19 outbreak) of 16,453 social media users living in mainland China. Computerized machine learning models were developed to estimate their daily scores of the nine dimensions of the Symptom Checklist-90 (SCL-90). Repeated measures analysis of variance (ANOVA) was used to compare the SCL-90 dimension scores between Wuhan and non-Wuhan residents. Fixed effect models were used to analyze the relation of the estimated SCL-90 scores with the daily reported cumulative case numbers and lasting time of the epidemic among Wuhan and non-Wuhan users. In non-Wuhan users, the estimated scores for all the SCL-90 dimensions significantly increased with the lasting time of the epidemic and the accumulation of cases, except for the interpersonal sensitivity dimension. In Wuhan users, although the estimated scores for all nine SCL-90 dimensions significantly increased with the cumulative case numbers, the magnitude of the changes was generally smaller than that in non-Wuhan users. The mental health of Chinese Weibo users was affected by the daily updated information on case numbers and the lasting time of the COVID-19 outbreak.

COVID-19 Sentiment and the Chinese Stock Market: Evidence from the Official News Media and Sina Weibo.

This study quantitatively measures the Chinese stock market's reaction to sentiments regarding the Novel Coronavirus 2019 (COVID-19). Using 6.3 million items of textual data extracted from the official news media and Sina Weibo blogsite, we develop two COVID-19 sentiment indices that capture the moods related to COVID-19. Our sentiment indices are real-time and forward-looking indices in the stock market. We discover that stock returns and turnover rates were positively predicted by the COVID-19 sentiments during the period from December 17, 2019 to March 13, 2020. Consistent with this prediction, margin trading and short selling activities intensified proactively with growth sentiment. Overall, these results illustrate how the effects of the pandemic crisis were amplified by the sentiments.

Emotional Attitudes of Chinese Citizens on Social Distancing During the COVID-19 Outbreak: Analysis of Social Media Data.

BACKGROUND: Wuhan, China, the epicenter of the COVID-19 pandemic, imposed citywide lockdown measures on January 23, 2020. Neighboring cities in Hubei Province followed suit with the government enforcing social distancing measures to restrict the spread of the disease throughout the province. Few studies have examined the emotional attitudes of citizens as expressed on social media toward the imposed social distancing measures and the factors that affected their emotions. OBJECTIVE: The aim of this study was twofold. First, we aimed to detect the emotional attitudes of different groups of users on Sina Weibo toward the social distancing measures imposed by the People's Government of Hubei Province. Second, the influencing factors of their emotions, as well as the impact of the imposed measures on users' emotions, was studied. METHODS: Sina Weibo, one of China's largest social media platforms, was chosen as the primary data source. The time span of selected data was from January 21, 2020, to March 24, 2020, while analysis was completed in late June 2020. Bi-directional long short-term memory (Bi-LSTM) was used to analyze users' emotions, while logistic regression analysis was employed to explore the influence of explanatory variables on users' emotions, such as age and spatial location. Further, the moderating effects of social distancing measures on the relationship between user characteristics and users' emotions were assessed by observing the interaction effects between the measures and explanatory variables. RESULTS: Based on the 63,169 comments obtained, we identified six topics of discussion-(1) delaying the resumption of work and school, (2) travel restrictions, (3) traffic restrictions, (4) extending the Lunar New Year holiday, (5) closing public spaces, and (6) community containment. There was no multicollinearity in the data during statistical analysis; the Hosmer-Lemeshow goodness-of-fit was 0.24 (χ28=10.34, P>.24). The main emotions shown by citizens were negative, including anger and fear. Users located in Hubei Province showed the highest amount of negative emotions in Mainland China. There are statistically significant differences in the distribution of emotional polarity between social distancing measures (χ220=19,084.73, P<.001), as well as emotional polarity between genders (χ24=1784.59, P<.001) and emotional polarity between spatial locations (χ24=1659.67, P<.001). Compared with other types of social distancing measures, the measures of delaying the resumption of work and school or travel restrictions mainly had a positive moderating effect on public emotion, while traffic restrictions or community containment had a negative moderating effect on public emotion. CONCLUSIONS: Findings provide a reference point for the adoption of epidemic prevention and control measures, and are considered helpful for government agencies to take timely actions to alleviate negative emotions during public health emergencies.

A Novel Machine Learning Framework for Comparison of Viral COVID-19-Related Sina Weibo and Twitter Posts: Workflow Development and Content Analysis.

BACKGROUND: Social media plays a critical role in health communications, especially during global health emergencies such as the current COVID-19 pandemic. However, there is a lack of a universal analytical framework to extract, quantify, and compare content features in public discourse of emerging health issues on different social media platforms across a broad sociocultural spectrum. OBJECTIVE: We aimed to develop a novel and universal content feature extraction and analytical framework and contrast how content features differ with sociocultural background in discussions of the emerging COVID-19 global health crisis on major social media platforms. METHODS: We sampled the 1000 most shared viral Twitter and Sina Weibo posts regarding COVID-19, developed a comprehensive coding scheme to identify 77 potential features across six major categories (eg, clinical and epidemiological, countermeasures, politics and policy, responses), quantified feature values (0 or 1, indicating whether or not the content feature is mentioned in the post) in each viral post across social media platforms, and performed subsequent comparative analyses. Machine learning dimension reduction and clustering analysis were then applied to harness the power of social media data and provide more unbiased characterization of web-based health communications. RESULTS: There were substantially different distributions, prevalence, and associations of content features in public discourse about the COVID-19 pandemic on the two social media platforms. Weibo users were more likely to focus on the disease itself and health aspects, while Twitter users engaged more about policy, politics, and other societal issues. CONCLUSIONS: We extracted a rich set of content features from social media data to accurately characterize public discourse related to COVID-19 in different sociocultural backgrounds. In addition, this universal framework can be adopted to analyze social media discussions of other emerging health issues beyond the COVID-19 pandemic.

Concerns Expressed by Chinese Social Media Users During the COVID-19 Pandemic: Content Analysis of Sina Weibo Microblogging Data.

BACKGROUND: The COVID-19 pandemic has created a global health crisis that is affecting economies and societies worldwide. During times of uncertainty and unexpected change, people have turned to social media platforms as communication tools and primary information sources. Platforms such as Twitter and Sina Weibo have allowed communities to share discussion and emotional support; they also play important roles for individuals, governments, and organizations in exchanging information and expressing opinions. However, research that studies the main concerns expressed by social media users during the pandemic is limited. OBJECTIVE: The aim of this study was to examine the main concerns raised and discussed by citizens on Sina Weibo, the largest social media platform in China, during the COVID-19 pandemic. METHODS: We used a web crawler tool and a set of predefined search terms (New Coronavirus Pneumonia, New Coronavirus, and COVID-19) to investigate concerns raised by Sina Weibo users. Textual information and metadata (number of likes, comments, retweets, publishing time, and publishing location) of microblog posts published between December 1, 2019, and July 32, 2020, were collected. After segmenting the words of the collected text, we used a topic modeling technique, latent Dirichlet allocation (LDA), to identify the most common topics posted by users. We analyzed the emotional tendencies of the topics, calculated the proportional distribution of the topics, performed user behavior analysis on the topics using data collected from the number of likes, comments, and retweets, and studied the changes in user concerns and differences in participation between citizens living in different regions of mainland China. RESULTS: Based on the 203,191 eligible microblog posts collected, we identified 17 topics and grouped them into 8 themes. These topics were pandemic statistics, domestic epidemic, epidemics in other countries worldwide, COVID-19 treatments, medical resources, economic shock, quarantine and investigation, patients' outcry for help, work and production resumption, psychological influence, joint prevention and control, material donation, epidemics in neighboring countries, vaccine development, fueling and saluting antiepidemic action, detection, and study resumption. The mean sentiment was positive for 11 topics and negative for 6 topics. The topic with the highest mean of retweets was domestic epidemic, while the topic with the highest mean of likes was quarantine and investigation. CONCLUSIONS: Concerns expressed by social media users are highly correlated with the evolution of the global pandemic. During the COVID-19 pandemic, social media has provided a platform for Chinese government departments and organizations to better understand public concerns and demands. Similarly, social media has provided channels to disseminate information about epidemic prevention and has influenced public attitudes and behaviors. Government departments, especially those related to health, can create appropriate policies in a timely manner through monitoring social media platforms to guide public opinion and behavior during epidemics.

Mining the Characteristics of COVID-19 Patients in China: Analysis of Social Media Posts.

BACKGROUND: In December 2019, pneumonia cases of unknown origin were reported in Wuhan City, Hubei Province, China. Identified as the coronavirus disease (COVID-19), the number of cases grew rapidly by human-to-human transmission in Wuhan. Social media, especially Sina Weibo (a major Chinese microblogging social media site), has become an important platform for the public to obtain information and seek help. OBJECTIVE: This study aims to analyze the characteristics of suspected or laboratory-confirmed COVID-19 patients who asked for help on Sina Weibo. METHODS: We conducted data mining on Sina Weibo and extracted the data of 485 patients who presented with clinical symptoms and imaging descriptions of suspected or laboratory-confirmed cases of COVID-19. In total, 9878 posts seeking help on Sina Weibo from February 3 to 20, 2020 were analyzed. We used a descriptive research methodology to describe the distribution and other epidemiological characteristics of patients with suspected or laboratory-confirmed SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) infection. The distance between patients' home and the nearest designated hospital was calculated using the geographic information system ArcGIS. RESULTS: All patients included in this study who sought help on Sina Weibo lived in Wuhan, with a median age of 63.0 years (IQR 55.0-71.0). Fever (408/485, 84.12%) was the most common symptom. Ground-glass opacity (237/314, 75.48%) was the most common pattern on chest computed tomography; 39.67% (167/421) of families had suspected and/or laboratory-confirmed family members; 36.58% (154/421) of families had 1 or 2 suspected and/or laboratory-confirmed members; and 70.52% (232/329) of patients needed to rely on their relatives for help. The median time from illness onset to real-time reverse transcription-polymerase chain reaction (RT-PCR) testing was 8 days (IQR 5.0-10.0), and the median time from illness onset to online help was 10 days (IQR 6.0-12.0). Of 481 patients, 32.22% (n=155) lived more than 3 kilometers away from the nearest designated hospital. CONCLUSIONS: Our findings show that patients seeking help on Sina Weibo lived in Wuhan and most were elderly. Most patients had fever symptoms, and ground-glass opacities were noted in chest computed tomography. The onset of the disease was characterized by family clustering and most families lived far from the designated hospital. Therefore, we recommend the following: (1) the most stringent centralized medical observation measures should be taken to avoid transmission in family clusters; and (2) social media can help these patients get early attention during Wuhan's lockdown. These findings can help the government and the health department identify high-risk patients and accelerate emergency responses following public demands for help.