Background: Media coverage and scholarly research have reported that Asian people who reside in the United States have been the targets of racially motivated incidents during the COVID-19 pandemic. Objective: This study aimed to examine the types of discrimination and worries experienced by Asians and Asian Americans living in the United States during the pandemic, as well as factors that were associated with everyday discrimination experience and concerns about future discrimination that the Asian community may face. Methods: A cross-sectional online survey was conducted. A total of 235 people who identified themselves as Asian or Asian American and resided in the United States completed the questionnaire. Results: Our study suggested that up to a third of Asians surveyed had experienced some type of discrimination. Pooling the responses "very often," "often," and "sometimes," the percentages for each experienced discrimination type ranged between 14%-34%. In total, 49%-58% of respondents expressed concerns about discrimination in the future. The most frequently experienced discrimination types, as indicated by responses "very often" and "often," were "people act as if they think you are dangerous" (25/235, 11%) and "being treated with less courtesy or respect" (24/235, 10%). About 14% (32/235) of individuals reported very often, often, or sometimes being threatened or harassed. In addition, social media use was significantly associated with a higher likelihood of experiencing discrimination (beta=.18, P=.01) and having concerns about future episodes of discrimination the community may face (beta=.20, P=.005). Use of print media was also positively associated with experiencing discrimination (beta=.31, P<.001). Conclusions: Our study provided important empirical evidence regarding the various types of discrimination Asians residing in the United States experienced or worried about during the COVID-19 pandemic. The relationship between media sources and the perception of racial biases in this group was also identified. We noted the role of social media in reinforcing the perception of discrimination experience and concerns about future discrimination among Asians during this outbreak. Our results indicate several practical implications for public health agencies. To reduce discrimination against Asians during the pandemic, official sources and public health professionals should be cognizant of the possible impacts of stigmatizing cues in media reports on activating racial biases. Furthermore, Asians or Asian Americans could also be informed that using social media to obtain COVID-19 information is associated with an increase in concerns about future discrimination, and thus they may consider approaching this media source with caution.
Background: Family violence (including intimate partner violence/domestic violence, child abuse, and elder abuse) is a hidden pandemic happening alongside COVID-19. The rates of family violence are rising fast, and women and children are disproportionately affected and vulnerable during this time. Objective: This study aims to provide a large-scale analysis of public discourse on family violence and the COVID-19 pandemic on Twitter. Methods: We analyzed over 1 million tweets related to family violence and COVID-19 from April 12 to July 16, 2020. We used the machine learning approach Latent Dirichlet Allocation and identified salient themes, topics, and representative tweets. Results: We extracted 9 themes from 1,015,874 tweets on family violence and the COVID-19 pandemic: (1) increased vulnerability: COVID-19 and family violence (eg, rising rates, increases in hotline calls, homicide); (2) types of family violence (eg, child abuse, domestic violence, sexual abuse); (3) forms of family violence (eg, physical aggression, coercive control); (4) risk factors linked to family violence (eg, alcohol abuse, financial constraints, guns, quarantine); (5) victims of family violence (eg, the LGBTQ [lesbian, gay, bisexual, transgender, and queer or questioning] community, women, women of color, children); (6) social services for family violence (eg, hotlines, social workers, confidential services, shelters, funding); (7) law enforcement response (eg, 911 calls, police arrest, protective orders, abuse reports); (8) social movements and awareness (eg, support victims, raise awareness); and (9) domestic violence-related news (eg, Tara Reade, Melissa DeRosa). Conclusions: This study overcomes limitations in the existing scholarship where data on the consequences of COVID-19 on family violence are lacking. We contribute to understanding family violence during the pandemic by providing surveillance via tweets. This is essential for identifying potentially useful policy programs that can offer targeted support for victims and survivors as we prepare for future outbreaks.
Background: Since the outbreak of COVID-19, the Chinese government and the Chinese Center for Disease Control and Prevention have released COVID-19-related information to the public through various channels to raise their concern level of the pandemic, increase their knowledge of disease prevention, and ensure the uptake of proper preventive practices. Objective: Our objectives were to determine Chinese netizens' concerns related to COVID-19 and the relationship between their concerns and information on the internet. We also aimed to elucidate the association between individuals' levels of concern, knowledge, and behaviors related to COVID-19. Methods: The questionnaire, which consisted of 15 closed-ended questions, was designed to investigate Chinese netizens' knowledge about COVID-19. The self-selection online survey method of nonprobability sampling was used to recruit participants through Dingxiangyisheng WeChat (a public, medical, and health service platform in China) accounts. Standard descriptive statistics and multivariate logistic regression analyses were conducted to analyze the data. Results: In total, 10,304 respondents were surveyed on the internet (response rate=1.75%; 10,304/590,000). Nearly all (n=9803, 95.30%) participants were concerned about "confirmed cases" of COVID-19, and 87.70% (n=9036) received information about the outbreak through social media websites. There were significant differences in participants' concerns by sex (P=.02), age (P<.001), educational attainment (P=.001), and occupation (P<.001). All knowledge questions and preventive practices were associated with concerns about COVID-19. The results of the multivariate logistic regression indicated that participants' sex, educational attainment, occupation and employment status, knowledge acquisition, and concern level were significantly associated with the practice of proper preventive behaviors. Conclusions: This study elucidated Chinese netizens' concerns, information sources, and preventive behaviors related to the COVID-19 pandemic. Sex, educational attainment, occupation and employment status, knowledge acquisition, and level of concern were key factors associated with proper preventive behaviors. This offers a theoretical basis for the government to provide targeted disease prevention and control information to the public.
Background: With the advancement of technology, more countries are now adopting the use of electronic product information (ePI), which refer to an electronic version of physical product inserts in a semistructured format optimized for electronic manipulation. The successful implementation of ePI has led to advantages and convenience to patients, health care professionals, and pharmaceutical companies in many regions and countries. In the Hong Kong Special Administrative Region (SAR), there is currently no citywide implementation of ePI. The SAR exhibits conditions that would favor the implementation of an ePI system, as well as existing barriers hindering its implementation. However, no study has been performed to examine the specific situation in Hong Kong. Objective: The objective of this study is to explore working pharmacists' overall perception of ePI and to identify potential challenges to the implementation of an ePI system in Hong Kong. Methods: This mixed-method study involved a structured survey and interview with practicing pharmacists in Hong Kong. Pharmacists were eligible if they were licensed to practice in Hong Kong, and currently working locally in any pharmacy-related sectors and institutions. Respondents completed a survey to indicate their level of agreement with statements regarding the potential advantages of ePI over paper PI. A structured interview was conducted to gather respondents' perceived advantages of ePI over paper PI in different aspects, such as professionalism, usability, presentation, and environment, as well as challenges of citywide ePI implementation in Hong Kong. Thematic analysis was adopted to analyze the qualitative data. Grounded theory was used to generate themes and identify specific outcomes. Results: A total of 16 pharmacists were recruited, comprising 4 community pharmacists, 5 hospital pharmacists, and 7 industrial pharmacists. All of them used electronic platforms at least once per month on average. Respondents identified many flaws in physical package inserts that can potentially be mitigated using ePI. The speed with which drug information can be retrieved and the degree to which the drug information can be readily updated and disseminated were considered the greatest strengths of ePI. The clarity with which ePI present drug information to patients was considered as the weakest aspect of ePI. Many respondents highlighted concerns about the security risks and high cost associated with system maintenance and that certain subpopulations may not be sufficiently computer literate to navigate the ePI system. Respondents also voiced many concerns about the implementation and maintenance of a local ePI system. Conclusions: We conclude that an ePI system is generally supported by pharmacists but concerns about implementation process and maintenance of the system has been raised. The perceived benefits of ePI gathered from this study, as well as collective evidence from other countries with mature ePI systems, confirm that more efforts should be made to promote optimized development and implementation of an ePI system in Hong Kong.
Background: Patients with COVID-19 in the intensive care unit (ICU) have a high mortality rate, and methods to assess patients' prognosis early and administer precise treatment are of great significance. Objective: The aim of this study was to use machine learning to construct a model for the analysis of risk factors and prediction of mortality among ICU patients with COVID-19. Methods: In this study, 123 patients with COVID-19 in the ICU of Vulcan Hill Hospital were retrospectively selected from the database, and the data were randomly divided into a training data set (n=98) and test data set (n=25) with a 4:1 ratio. Significance tests, correlation analysis, and factor analysis were used to screen 100 potential risk factors individually. Conventional logistic regression methods and four machine learning algorithms were used to construct the risk prediction model for the prognosis of patients with COVID-19 in the ICU. The performance of these machine learning models was measured by the area under the receiver operating characteristic curve (AUC). Interpretation and evaluation of the risk prediction model were performed using calibration curves, SHapley Additive exPlanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), etc, to ensure its stability and reliability. The outcome was based on the ICU deaths recorded from the database. Results: Layer-by-layer screening of 100 potential risk factors finally revealed 8 important risk factors that were included in the risk prediction model: lymphocyte percentage, prothrombin time, lactate dehydrogenase, total bilirubin, eosinophil percentage, creatinine, neutrophil percentage, and albumin level. Finally, an eXtreme Gradient Boosting (XGBoost) model established with the 8 important risk factors showed the best recognition ability in the training set of 5-fold cross validation (AUC=0.86) and the verification queue (AUC=0.92). The calibration curve showed that the risk predicted by the model was in good agreement with the actual risk. In addition, using the SHAP and LIME algorithms, feature interpretation and sample prediction interpretation algorithms of the XGBoost black box model were implemented. Additionally, the model was translated into a web-based risk calculator that is freely available for public usage. Conclusions: The 8-factor XGBoost model predicts risk of death in ICU patients with COVID-19 well; it initially demonstrates stability and can be used effectively to predict COVID-19 prognosis in ICU patients.
Background: The novel COVID-19 disease has spread worldwide, resulting in a new pandemic. The Chinese government implemented strong intervention measures in the early stage of the epidemic, including strict travel bans and social distancing policies. Prioritizing the analysis of different contributing factors to outbreak outcomes is important for the precise prevention and control of infectious diseases. We proposed a novel framework for resolving this issue and applied it to data from China. Objective: This study aimed to systematically identify national-level and city-level contributing factors to the control of COVID-19 in China. Methods: Daily COVID-19 case data and related multidimensional data, including travel-related, medical, socioeconomic, environmental, and influenza-like illness factors, from 343 cities in China were collected. A correlation analysis and interpretable machine learning algorithm were used to evaluate the quantitative contribution of factors to new cases and COVID-19 growth rates during the epidemic period (ie, January 17 to February 29, 2020). Results: Many factors correlated with the spread of COVID-19 in China. Travel-related population movement was the main contributing factor for new cases and COVID-19 growth rates in China, and its contributions were as high as 77% and 41%, respectively. There was a clear lag effect for travel-related factors (previous vs current week: new cases, 45% vs 32%; COVID-19 growth rates, 21% vs 20%). Travel from non-Wuhan regions was the single factor with the most significant impact on COVID-19 growth rates (contribution: new cases, 12%; COVID-19 growth rate, 26%), and its contribution could not be ignored. City flow, a measure of outbreak control strength, contributed 16% and 7% to new cases and COVID-19 growth rates, respectively. Socioeconomic factors also played important roles in COVID-19 growth rates in China (contribution, 28%). Other factors, including medical, environmental, and influenza-like illness factors, also contributed to new cases and COVID-19 growth rates in China. Based on our analysis of individual cities, compared to Beijing, population flow from Wuhan and internal flow within Wenzhou were driving factors for increasing the number of new cases in Wenzhou. For Chongqing, the main contributing factor for new cases was population flow from Hubei, beyond Wuhan. The high COVID-19 growth rates in Wenzhou were driven by population-related factors. Conclusions: Many factors contributed to the COVID-19 outbreak outcomes in China. The differential effects of various factors, including specific city-level factors, emphasize the importance of precise, targeted strategies for controlling the COVID-19 outbreak and future infectious disease outbreaks.
Background: As human society enters an era of vast and easily accessible social media, a growing number of people are exploiting the internet to search and exchange medical information. Because internet search data could reflect population interest in particular health topics, they provide a new way of understanding health concerns regarding noncommunicable diseases (NCDs) and the role they play in their prevention. Objective: We aimed to explore the association of internet search data for NCDs with published disease incidence and mortality rates in the United States and to grasp the health concerns toward NCDs. Methods: We tracked NCDs by examining the correlations among the incidence rates, mortality rates, and internet searches in the United States from 2004 to 2017, and we established forecast models based on the relationship between the disease rates and internet searches. Results: Incidence and mortality rates of 29 diseases in the United States were statistically significantly correlated with the relative search volumes (RSVs) of their search terms (P<.05). From the perspective of the goodness of fit of the multiple regression prediction models, the results were closest to 1 for diabetes mellitus, stroke, atrial fibrillation and flutter, Hodgkin lymphoma, and testicular cancer; the coefficients of determination of their linear regression models for predicting incidence were 80%, 88%, 96%, 80%, and 78%, respectively. Meanwhile, the coefficient of determination of their linear regression models for predicting mortality was 82%, 62%, 94%, 78%, and 62%, respectively. Conclusions: An advanced understanding of search behaviors could augment traditional epidemiologic surveillance and could be used as a reference to aid in disease prediction and prevention.
Background: So far, there have been no published population studies on the relationship between a COVID-19 infection and public risk perception, information source, knowledge, attitude, and behaviors during the COVID-19 outbreak in China. Objective: This study aims to understand the relationships between COVID-19 infection; four personal nonpharmaceutical interventions (NPIs; handwashing, proper coughing habits, social distancing, and mask wearing); and public risk perception, knowledge, attitude, and other social demographic variables. Methods: An online survey of 8158 Chinese adults between February 22 and March 5, 2020, was conducted. Bivariate associations between categorical variables were examined using Fisher exact test. We also explored the determinants of four NPIs as well as their association with COVID-19 infection using logistic regression. Results: Of 8158 adults included, 57 (0.73%) were infected with COVID-19. The overwhelming majority of respondents showed a positive attitude (n=8094, 99.2%), positive risk perception (n=8146, 99.9%), and high knowledge levels that were among the strongest predictors of the four adopted NPIs (handwashing: n=7895, 96.8%; proper coughing: 5997/6444, 93.1%; social distancing: n=7104/8158, 87.1%; and mask wearing: 5011/5120, 97.9%). There was an increased risk of COVID-19 infection for those who did not wash their hands (2.28% vs 0.65%; risk ratio [RR] 3.53, 95% CI 1.53-8.15; P=.009), did not practice proper coughing (1.79% vs 0.73%; RR 2.44, 95% CI 1.15-5.15; P=.03), did not practice social distancing (1.52% vs 0.58%; RR 2.63, 95% CI 1.48-4.67; P=.002), and did not wear a mask (7.41% vs 0.6%; RR 12.38, 95% CI 5.81-26.36; P<.001). For those who did practice all other three NPIs, wearing a mask was associated with a significantly reduced risk of infection compared to those who did not wear a mask (0.6% vs 16.7%; P=.04). Similarly, for those who did not practice all or part of the other three NPIs, wearing a mask was also associated with a significantly reduced risk of infection. In a penalized logistic regression model including all four NPIs, wearing a mask was the only significant predictor of COVID-19 infection among the four NPIs (odds ratio 7.20, 95% CI 2.24-23.11; P<.001). Conclusions: We found high levels of risk perception, positive attitude, desirable knowledge, as well as a high level of adopting the four NPIs. The relevant knowledge, risk perception, and attitude were strong predictors of adapting the four NPIs. Mask wearing, among the four personal NPIs, was the most effective protective measure against COVID-19 infection, with added preventive effect among those who practiced all or part of the other three NPIs.
Background: Online misinformation proliferation during the COVID-19 pandemic has become a major public health concern. Objective: We aimed to assess the prevalence of COVID-19 misinformation exposure and beliefs, associated factors including psychological distress with misinformation exposure, and the associations between COVID-19 knowledge and number of preventive behaviors. Methods: A cross-sectional online survey was conducted with 1049 South Korean adults in April 2020. Respondents were asked about receiving COVID-19 misinformation using 12 items identified by the World Health Organization. Logistic regression was used to compute adjusted odds ratios (aORs) for the association of receiving misinformation with sociodemographic characteristics, source of information, COVID-19 misinformation belief, and psychological distress, as well as the associations of COVID-19 misinformation belief with COVID-19 knowledge and the number of COVID-19 preventive behaviors among those who received the misinformation. All data were weighted according to the Korea census data in 2018. Results: Overall, 67.78% (n=711) of respondents reported exposure to at least one COVID-19 misinformation item. Misinformation exposure was associated with younger age, higher education levels, and lower income. Sources of information associated with misinformation exposure were social networking services (aOR 1.67, 95% CI 1.20-2.32) and instant messaging (aOR 1.79, 1.27-2.51). Misinformation exposure was also associated with psychological distress including anxiety (aOR 1.80, 1.24-2.61), depressive (aOR 1.47, 1.09-2.00), and posttraumatic stress disorder symptoms (aOR 1.97, 1.42-2.73), as well as misinformation belief (aOR 7.33, 5.17-10.38). Misinformation belief was associated with poorer COVID-19 knowledge (high: aOR 0.62, 0.45-0.84) and fewer preventive behaviors (>= 7 behaviors: aOR 0.54, 0.39-0.74). Conclusions: COVID-19 misinformation exposure was associated with misinformation belief, while misinformation belief was associated with fewer preventive behaviors. Given the potential of misinformation to undermine global efforts in COVID-19 disease control, up-to-date public health strategies are required to counter the proliferation of misinformation.
Background: In the context of the COVID-19 infodemic, the global profusion of monikers and hashtags for COVID-19 have found their way into daily communication and contributed to a backlash against China and the Chinese people. Objective: This study examines public engagement in crisis communication about COVID-19 during the early epidemic stage and the practical strategy of social mobilization to mitigate the infodemic. Methods: We retrieved the unbiased values of the top-ranked search phrases between December 30, 2019, and July 15, 2020, which normalized the anonymized, categorized, and aggregated samples from Google Search data. This study illustrates the most-searched terms, including the official COVID-19 terms, the stigmatized terms, and other controls, to measure the collective behavioral propensities to stigmatized terms and to explore the global reaction to the COVID-19 epidemic in the real world. We calculated the ratio of the cumulative number of COVID-19 cases to the regional population as the cumulative rate (R) of a specific country or territory and calculated the Gini coefficient (G) to measure the collective heterogeneity of crowd behavior. Results: People around the world are using stigmatizing terms on Google Search, and these terms were used earlier than the official names. Many stigmatized monikers against China (eg, "Wuhan pneumonia," G=0.73; "Wuhan coronavirus," G=0.60; "China pneumonia," G=0.59; "China coronavirus," G=0.52; "Chinese coronavirus," G=0.50) had high collective heterogeneity of crowd behavior between December 30, 2019, and July 15, 2020, while the official terms "COVID-19" (G=0.44) and "SARS-CoV-2" (G=0.42) have not become de facto standard usages. Moreover, the pattern of high consistent usage was observed in 13 territories with low cumulative rates (R) between January 16 and July 15, 2020, out of 58 countries and territories that have reported confirmed cases of COVID-19. In the scientific literature, multifarious naming practices may have provoked unintended negative impacts by stigmatizing Chinese people. The World Health Organization; the United Nations Educational, Scientific and Cultural Organization; and the media initiated campaigns for fighting back against the COVID-19 infodemic with the same mission but in diverse voices. Conclusions: Infodemiological analysis can articulate the collective propensities to stigmatized monikers across search behaviors, which may reflect the collective sentiment of backlash against China and Chinese people in the real world. The full-fledged official terms are expected to fight back against the resilience of negative perceptual bias amid the COVID-19 epidemic. Such official naming efforts against the infodemic should be met with a fair share of identification in scientific conventions and sociocultural paradigms. As an integral component of preparedness, appropriate nomenclatures should be duly assigned to the newly identified coronavirus, and social mobilization in a uniform voice is a priority for combating the next infodemic.
Background: The outbreak of COVID-19 began in 2019 and is expected to impact the psychological health of college students. Few studies have investigated the associations among health risk communication, social media, and psychological symptoms during a major pandemic. Objective: The aim of this research was to assess the prevalence of psychological symptoms among college students and explore their associations with health risk communication and social media. Methods: A web-based survey was distributed through the Wenjuanxing platform among Chinese college students from March 3-15, 2020. In addition to demographics, information on health risk communication and social media was collected, and the Symptom Checklist 90 Phobia and Health Anxiety Inventory subscale was used to assess psychological symptoms among 1676 college students in China Multivariable logistic regression was performed to examine these independent risk factors. Results: The prevalence of panic and health anxiety was 17.2% (288/1676) and 24.3% (408/1676), respectively. Regarding risk communication, understanding the risk of COVID-19 (odds ratio [OR] 0.480, 95% CI 0.367-0.627) was a protective factor against panic. Knowledge of prognosis (OR 0.708, 95% CI 0.551-0.910), preventive measures (OR 0.380, 95% CI 0.195-0.742), and wearing face masks (OR 0.445, 95% CI 0.230-0.862) were shown to be protective factors in predicting health anxiety. Perceived lethality (OR 1.860, 95% CI 1.408-2.459), being affected by the global spread (OR 1.936, 95% CI 1.405-2.669), and impact on social contacts (OR 1.420, 95% CI 1.118-1.802) were identified as significant risk factors associated with health anxiety. In terms of social media, trust in mainstream media (OR 0.613, 95% CI 0.461-0.816) was considered to be a protective factor against health anxiety. Conclusions: There was a high prevalence of psychological symptoms among college students. Health risk communication and social media use were important in predicting psychological symptoms, especially health anxiety. Scientific and evidence-based information should be reported by social media platforms. Web-based consultation and intervention measures should be the focus of future studies.
Background: The outbreak of COVID-19 has caused a continuing global pandemic. Hospitals are integral to the control and prevention of COVID-19; however, they are facing numerous challenges during the epidemic. Objective: Our study aimed to introduce the practical experience of the design and implementation of a web-based COVID-19 service platform at a tertiary hospital in China as well as the preliminary results of the implementation. Methods: The web-based COVID-19 service platform was deployed within the health care system of the Guangdong Second Provincial General Hospital and Internet Hospital; the function of the platform was to provide web-based medical services for both members of the public and lay health care workers. The focal functions of this system included automated COVID-19 screening, related symptom monitoring, web-based consultation, and psychological support; it also served as a COVID-19 knowledge hub. The design and process of each function are introduced. The usage data for the platform service were collected and are represented by three periods: the pre-epidemic period (December 22, 2019, to January 22, 2020, 32 days), the controlled period (January 23 to March 31, 2020, 69 days), and the postepidemic period (April 1 to June 30, 2020, 91 days). Results: By the end of June 2020, 96,642 people had used the automated COVID-19 screening and symptom monitoring systems 161,884 and 7,795,194 times, respectively. The number of general web-based consultation services per day increased from 30 visits in the pre-epidemic period to 122 visits during the controlled period, then dropped to 73 visits in the postepidemic period. The psychological counseling program served 636 clients during the epidemic period. For people who used the automated COVID-19 screening service, 160,916 (99.40%) of the total users were classified in the no risk category. 464 (0.29%) of the people were categorized as medium to high risk, and 12 people (0.01%) were recommended for further COVID-19 testing and treatment. Among the 96,642 individuals who used the COVID-19 related symptoms monitoring service, 6696 (6.93%) were symptomatic at some point during the monitoring period. Fever was the most frequently reported symptom, with 2684/6696 symptomatic people (40.1%) having had this symptom. Cough and sore throat were also relatively frequently reported by the 6696 symptomatic users (1657 people, 24.7%, and 1622 people, 24.2%, respectively). Conclusions: The web-based COVID-19 service platform implemented at a tertiary hospital in China is exhibited to be a role model for using digital health technologies to respond to the COVID-19 pandemic. The digital solutions of automated COVID-19 screening, daily symptom monitoring, web-based care, and knowledge propagation have plausible acceptability and feasibility for complementing offline hospital services and facilitating disease control and prevention.
Background: It is important to measure the public response to the COVID-19 pandemic. Twitter is an important data source for infodemiology studies involving public response monitoring. Objective: The objective of this study is to examine COVID-19-related discussions, concerns, and sentiments using tweets posted by Twitter users. Methods: We analyzed 4 million Twitter messages related to the COVID-19 pandemic using a list of 20 hashtags (eg, "coronavirus," "COVID-19," "quarantine") from March 7 to April 21, 2020. We used a machine learning approach, Latent Dirichlet Allocation (LDA), to identify popular unigrams and bigrams, salient topics and themes, and sentiments in the collected tweets. Results: Popular unigrams included "virus," "lockdown," and "quarantine." Popular bigrams included "COVID-19," "stay home," "corona virus," "social distancing," and "new cases." We identified 13 discussion topics and categorized them into 5 different themes: (1) public health measures to slow the spread of COVID-19, (2) social stigma associated with COVID-19, (3) COVID-19 news, cases, and deaths, (4) COVID-19 in the United States, and (5) COVID-19 in the rest of the world. Across all identified topics, the dominant sentiments for the spread of COVID-19 were anticipation that measures can be taken, followed by mixed feelings of trust, anger, and fear related to different topics. The public tweets revealed a significant feeling of fear when people discussed new COVID-19 cases and deaths compared to other topics. Conclusions: This study showed that Twitter data and machine learning approaches can be leveraged for an infodemiology study, enabling research into evolving public discussions and sentiments during the COVID-19 pandemic. As the situation rapidly evolves, several topics are consistently dominant on Twitter, such as confirmed cases and death rates, preventive measures, health authorities and government policies, COVID-19 stigma, and negative psychological reactions (eg, fear). Real-time monitoring and assessment of Twitter discussions and concerns could provide useful data for public health emergency responses and planning Pandemic-related fear, stigma, and mental health concerns are already evident and may continue to influence public trust when a second wave of COVID-19 occurs or there is a new surge of the current pandemic.
Background: Various online rumors have led to inappropriate behaviors among the public in response to the COVID-19 epidemic in China These rumors adversely affect people's physical and mental health. Therefore, a better understanding of the relationship between public emotions and rumors during the epidemic may help generate useful strategies for guiding public emotions and dispelling rumors. Objective: This study aimed to explore whether public emotions are related to the dissemination of online rumors in the context of COVID-19. Methods: We used the web-crawling tool Scrapy to gather data published by People's Daily on Sina Weibo, a popular social media platform in China, after January 8, 2020. Netizens' comments under each Weibo post were collected. Nearly 1 million comments thus collected were divided into 5 categories: happiness, sadness, anger, fear, and neutral, based on the underlying emotional information identified and extracted from the comments by using a manual identification process. Data on rumors spread online were collected through Tencent's Jiaozhen platform. Time-lagged cross-correlation analyses were performed to examine the relationship between public emotions and rumors. Results: Our results indicated that the angrier the public felt, the more rumors there would likely be (r=0.48, P<.001) Similar results were observed for the relationship between fear and rumors (r=0.51, P<.001) and between sadness and rumors (r=0.47, P <.001). Furthermore, we found a positive correlation between happiness and rumors, with happiness lagging the emergence of rumors by 1 day (r=0.56, P<.001). In addition, our data showed a significant positive correlation between fear and fearful rumors (r=0.34, P=.02). Conclusions: Our findings confirm that public emotions are related to the rumors spread online in the context of COVID-19 in China Moreover, these findings provide several suggestions, such as the use of web-based monitoring methods, for relevant authorities and policy makers to guide public emotions and behavior during this public health emergency.