Electronic Cigarette Users' Perspective on the COVID-19 Pandemic: Observational Study Using Twitter Data

BackgroundPrevious studies have shown that electronic cigarette (e-cigarette) users might be more vulnerable to COVID-19 infection and could develop more severe symptoms if they contract the disease owing to their impaired immune responses to viral infections. Social media platforms such as Twitter have been widely used by individuals worldwide to express their responses to the current COVID-19 pandemic.ObjectiveIn this study, we aimed to examine the longitudinal changes in the attitudes of Twitter users who used e-cigarettes toward the COVID-19 pandemic, as well as compare differences in attitudes between e-cigarette users and nonusers based on Twitter data.MethodsThe study dataset containing COVID-19–related Twitter posts (tweets) posted between March 5 and April 3, 2020, was collected using a Twitter streaming application programming interface with COVID-19–related keywords. Twitter users were classified into two groups: Ecig group, including users who did not have commercial accounts but posted e-cigarette–related tweets between May 2019 and August 2019, and non-Ecig group, including users who did not post any e-cigarette–related tweets. Sentiment analysis was performed to compare sentiment scores towards the COVID-19 pandemic between both groups and determine whether the sentiment expressed was positive, negative, or neutral. Topic modeling was performed to compare the main topics discussed between the groups.ResultsThe US COVID-19 dataset consisted of 4,500,248 COVID-19–related tweets collected from 187,399 unique Twitter users in the Ecig group and 11,479,773 COVID-19–related tweets collected from 2,511,659 unique Twitter users in the non-Ecig group. Sentiment analysis showed that Ecig group users had more negative sentiment scores than non-Ecig group users. Results from topic modeling indicated that Ecig group users had more concerns about deaths due to COVID-19, whereas non-Ecig group users cared more about the government’s responses to the COVID-19 pandemic.ConclusionsOur findings show that Twitter users who tweeted about e-cigarettes had more concerns about the COVID-19 pandemic. These findings can inform public health practitioners to use social media platforms such as Twitter for timely monitoring of public responses to the COVID-19 pandemic and educating and encouraging current e-cigarette users to quit vaping to minimize the risks associated with COVID-19.     

“Thought I’d Share First” and Other Conspiracy Theory Tweets from the COVID-19 Infodemic: Exploratory Study

BackgroundThe COVID-19 outbreak has left many people isolated within their homes; these people are turning to social media for news and social connection, which leaves them vulnerable to believing and sharing misinformation. Health-related misinformation threatens adherence to public health messaging, and monitoring its spread on social media is critical to understanding the evolution of ideas that have potentially negative public health impacts.ObjectiveThe aim of this study is to use Twitter data to explore methods to characterize and classify four COVID-19 conspiracy theories and to provide context for each of these conspiracy theories through the first 5 months of the pandemic.MethodsWe began with a corpus of COVID-19 tweets (approximately 120 million) spanning late January to early May 2020. We first filtered tweets using regular expressions (n=1.8 million) and used random forest classification models to identify tweets related to four conspiracy theories. Our classified data sets were then used in downstream sentiment analysis and dynamic topic modeling to characterize the linguistic features of COVID-19 conspiracy theories as they evolve over time.ResultsAnalysis using model-labeled data was beneficial for increasing the proportion of data matching misinformation indicators. Random forest classifier metrics varied across the four conspiracy theories considered (F1 scores between 0.347 and 0.857); this performance increased as the given conspiracy theory was more narrowly defined. We showed that misinformation tweets demonstrate more negative sentiment when compared to nonmisinformation tweets and that theories evolve over time, incorporating details from unrelated conspiracy theories as well as real-world events.ConclusionsAlthough we focus here on health-related misinformation, this combination of approaches is not specific to public health and is valuable for characterizing misinformation in general, which is an important first step in creating targeted messaging to counteract its spread. Initial messaging should aim to preempt generalized misinformation before it becomes widespread, while later messaging will need to target evolving conspiracy theories and the new facets of each as they become incorporated.     

Factors Driving the Popularity and Virality of COVID-19 Vaccine Discourse on Twitter: Text Mining and Data Visualization Study

BackgroundCOVID-19 vaccination is considered a critical prevention measure to help end the pandemic. Social media platforms such as Twitter have played an important role in the public discussion about COVID-19 vaccines.ObjectiveThe aim of this study was to investigate message-level drivers of the popularity and virality of tweets about COVID-19 vaccines using machine-based text-mining techniques. We further aimed to examine the topic communities of the most liked and most retweeted tweets using network analysis and visualization.MethodsWe collected US-based English-language public tweets about COVID-19 vaccines from January 1, 2020, to April 30, 2021 (N=501,531). Topic modeling and sentiment analysis were used to identify latent topics and valence, which together with autoextracted information about media presence, linguistic features, and account verification were used in regression models to predict likes and retweets. Among the 2500 most liked tweets and 2500 most retweeted tweets, network analysis and visualization were used to detect topic communities and present the relationship between the topics and the tweets.ResultsTopic modeling yielded 12 topics. The regression analyses showed that 8 topics positively predicted likes and 7 topics positively predicted retweets, among which the topic of vaccine development and people’s views and that of vaccine efficacy and rollout had relatively larger effects. Network analysis and visualization revealed that the 2500 most liked and most retweeted retweets clustered around the topics of vaccine access, vaccine efficacy and rollout, vaccine development and people’s views, and vaccination status. The overall valence of the tweets was positive. Positive valence increased likes, but valence did not affect retweets. Media (photo, video, gif) presence and account verification increased likes and retweets. Linguistic features had mixed effects on likes and retweets.ConclusionsThis study suggests the public interest in and demand for information about vaccine development and people’s views, and about vaccine efficacy and rollout. These topics, along with the use of media and verified accounts, have enhanced the popularity and virality of tweets. These topics could be addressed in vaccine campaigns to help the diffusion of content on Twitter.     

Comparing News Articles and Tweets About COVID-19 in Brazil: Sentiment Analysis and Topic Modeling Approach

BackgroundThe COVID-19 pandemic is severely affecting people worldwide. Currently, an important approach to understand this phenomenon and its impact on the lives of people consists of monitoring social networks and news on the internet.ObjectiveThe purpose of this study is to present a methodology to capture the main subjects and themes under discussion in news media and social media and to apply this methodology to analyze the impact of the COVID-19 pandemic in Brazil.MethodsThis work proposes a methodology based on topic modeling, namely entity recognition, and sentiment analysis of texts to compare Twitter posts and news, followed by visualization of the evolution and impact of the COVID-19 pandemic. We focused our analysis on Brazil, an important epicenter of the pandemic; therefore, we faced the challenge of addressing Brazilian Portuguese texts.ResultsIn this work, we collected and analyzed 18,413 articles from news media and 1,597,934 tweets posted by 1,299,084 users in Brazil. The results show that the proposed methodology improved the topic sentiment analysis over time, enabling better monitoring of internet media. Additionally, with this tool, we extracted some interesting insights about the evolution of the COVID-19 pandemic in Brazil. For instance, we found that Twitter presented similar topic coverage to news media; the main entities were similar, but they differed in theme distribution and entity diversity. Moreover, some aspects represented negative sentiment toward political themes in both media, and a high incidence of mentions of a specific drug denoted high political polarization during the pandemic.ConclusionsThis study identified the main themes under discussion in both news and social media and how their sentiments evolved over time. It is possible to understand the major concerns of the public during the pandemic, and all the obtained information is thus useful for decision-making by authorities.     

Texas Public Agencies’ Tweets and Public Engagement During the COVID-19 Pandemic: Natural Language Processing Approach

BackgroundThe ongoing COVID-19 pandemic is characterized by different morbidity and mortality rates across different states, cities, rural areas, and diverse neighborhoods. The absence of a national strategy for battling the pandemic also leaves state and local governments responsible for creating their own response strategies and policies.ObjectiveThis study examines the content of COVID-19–related tweets posted by public health agencies in Texas and how content characteristics can predict the level of public engagement.MethodsAll COVID-19–related tweets (N=7269) posted by Texas public agencies during the first 6 months of 2020 were classified in terms of each tweet’s functions (whether the tweet provides information, promotes action, or builds community), the preventative measures mentioned, and the health beliefs discussed, by using natural language processing. Hierarchical linear regressions were conducted to explore how tweet content predicted public engagement.ResultsThe information function was the most prominent function, followed by the action or community functions. Beliefs regarding susceptibility, severity, and benefits were the most frequently covered health beliefs. Tweets that served the information or action functions were more likely to be retweeted, while tweets that served the action and community functions were more likely to be liked. Tweets that provided susceptibility information resulted in the most public engagement in terms of the number of retweets and likes.ConclusionsPublic health agencies should continue to use Twitter to disseminate information, promote action, and build communities. They need to improve their strategies for designing social media messages about the benefits of disease prevention behaviors and audiences’ self-efficacy.     

The Saudi Ministry of Health’s Twitter Communication Strategies and Public Engagement During the COVID-19 Pandemic: Content Analysis Study

BackgroundDuring a public health crisis such as the current COVID-19 pandemic, governments and health authorities need quick and accurate methods of communicating with the public. While social media can serve as a useful tool for effective communication during disease outbreaks, few studies have elucidated how these platforms are used by the Ministry of Health (MOH) during disease outbreaks in Saudi Arabia.ObjectiveGuided by the Crisis and Emergency Risk Communication model, this study aimed to explore the MOH’s use of Twitter and the public’s engagement during different stages of the COVID-19 pandemic in Saudi Arabia.MethodsTweets and corresponding likes and retweets were extracted from the official Twitter account of the MOH in Saudi Arabia for the period of January 1 through August 31, 2020. Tweets related to COVID-19 were identified; subsequently, content analysis was performed, in which tweets were coded for the following message types: risk messages, warnings, preparations, uncertainty reduction, efficacy, reassurance, and digital health responses. Public engagement was measured by examining the numbers of likes and retweets. The association between outbreak stages and types of messages was assessed, as well as the effect of these messages on public engagement.ResultsThe MOH posted a total of 1393 original tweets during the study period. Of the total tweets, 1293 (92.82%) were related to COVID-19, and 1217 were ultimately included in the analysis. The MOH posted the majority of its tweets (65.89%) during the initial stage of the outbreak. Accordingly, the public showed the highest level of engagement (as indicated by numbers of likes and retweets) during the initial stage. The types of messages sent by the MOH significantly differed across outbreak stages, with messages related to uncertainty reduction, reassurance, and efficacy being prevalent among all stages. Tweet content, media type, and crisis stage influenced the level of public engagement. Engagement was negatively associated with the inclusion of hyperlinks and multimedia files, while higher level of public engagement was associated with the use of hashtags. Tweets related to warnings, uncertainty reduction, and reassurance received high levels of public engagement.ConclusionsThis study provides insights into the Saudi MOH’s communication strategy during the COVID-19 pandemic. Our results have implications for researchers, governments, health organizations, and practitioners with regard to their communication practices during outbreaks. To increase public engagement, governments and health authorities should consider the public’s need for information. This, in turn, could raise public awareness regarding disease outbreaks.     

How Health Care Workers Wield Influence Through Twitter Hashtags: Retrospective Cross-sectional Study of the Gun Violence and COVID-19 Public Health Crises

BackgroundTwitter has emerged as a novel way for physicians to share ideas and advocate for policy change. #ThisIsOurLane (firearm injury) and #GetUsPPE (COVID-19) are examples of nationwide health care–led Twitter campaigns that went viral. Health care–initiated Twitter hashtags regarding major public health topics have gained national attention, but their content has not been systematically examined.ObjectiveWe hypothesized that Twitter discourse on two epidemics (firearm injury and COVID-19) would differ between tweets with health care–initiated hashtags (#ThisIsOurLane and #GetUsPPE) versus those with non–health care–initiated hashtags (#GunViolence and #COVID19).MethodsUsing natural language processing, we compared content, affect, and authorship of a random 1% of tweets using #ThisIsOurLane (Nov 2018-Oct 2019) and #GetUsPPE (March-May 2020), compared to #GunViolence and #COVID19 tweets, respectively. We extracted the relative frequency of single words and phrases and created two sets of features: (1) an open-vocabulary feature set to create 50 data-driven–determined word clusters to evaluate the content of tweets; and (2) a closed-vocabulary feature for psycholinguistic categorization among case and comparator tweets. In accordance with conventional linguistic analysis, we used a P<.001, after adjusting for multiple comparisons using the Bonferroni correction, to identify potentially meaningful correlations between language features and outcomes.ResultsIn total, 67% (n=4828) of #ThisIsOurLane tweets and 36.6% (n=7907) of #GetUsPPE tweets were authored by health care professionals, compared to 16% (n=1152) of #GunViolence and 9.8% (n=2117) of #COVID19 tweets. Tweets using #ThisIsOurLane and #GetUsPPE were more likely to contain health care–specific language; more language denoting positive emotions, affiliation, and group identity; and more action-oriented content compared to tweets with #GunViolence or #COVID19, respectively.ConclusionsTweets with health care–led hashtags expressed more positivity and more action-oriented language than the comparison hashtags. As social media is increasingly used for news discourse, public education, and grassroots organizing, the public health community can take advantage of social media’s broad reach to amplify truthful, actionable messages around public health issues.     

Public discourse and debate about vaccines in the midst of the covid-19 pandemic: A qualitative content analysis of Twitter

ObjectivesCharacterize the public debate and discourse about vaccines during the covid-19 vaccination programmes.MethodsWe performed a manual content analysis of a sample of English-written Twitter posts that included the word vaccine and its derivatives. We categorized 7 variables pertaining to the content of the posts, and classified the type of user that published the post and the number of retweets. Then, the patterns of association between these variables were further explored.ResultsAmong the tweets with negative tone towards vaccines, 33% display negationist discourses, 29% protest or defiance discourses, 13% discuss the pandemic management measures and yet another 13% of these tweets display a scientific discourse. Research results, vaccination data and practical information are more associated to positive tone towards vaccines, while news relate to neutral tone. The users that received more retweets were media accounts and journalists, followed by government accounts and scientific organizations related to the government. Tweets displaying preventive messages received more retweets in average. The discourses most associated with objective information are the preventive, institutional, medical-scientific, and those about the different measures to manage the pandemic. On the other hand, the most subjective tweets are those with negationist, antinegationist and protest discourses.ConclusionsAlthough there is a non-negligible proportion of tweets that are directly opposed to vaccines, also an important part of vaccine-negative content takes the form of protest discourses, criticisms towards government actions as well as towards the measures to tackle the pandemic. Therefore, negative discourses during the pandemic included serious vaccine hesitancy cases. Moreover, they were not only fuelled by distrust in science, but also and very importantly they were connected to dissatisfaction towards the public management of the pandemic.     

“Any idea how fast ‘It’s just a mask!’ can turn into ‘It’s just a vaccine!’”: From mask mandates to vaccine mandates during the COVID-19 pandemic

Protests starting in the summer of 2020, notedly in the US and UK, have brought together two constituencies: pre-existing anti-vaccine groups and newly formed oppositional COVID-19 groups. The oppositional COVID-19 groups vary in composition and nature, but the central focus is a disagreement about the seriousness and threat of COVID-19 and with the public health measures to control COVID-19. What unites many disparate interests is an aversion to mandates. The compulsion to undertake particular public health activities such as mask-wearing and vaccination is a complex topic of public attitudes and beliefs alongside public health goals and messaging. We aim to analyse social media discussions about facemask wearing and the adoption of potential vaccines for COVID-19.Using media monitoring software Meltwater^TM, we analyse English-language tweets for one year from 1st June 2020 until 1st June 2021. We pay particular attention to connections in conversations between key topics of concern regarding masks and vaccines across social media networks. We track where ideas and activist behaviours towards both health interventions have originated, have similarities, and how they have changed over time.Our aim is to provide an overview of the key trends and themes of discussion concerning attitudes to and adoption of health measures in the control of COVID-19 and how publics react when confronted with mandatory policies. We draw on an already extensive literature about mandatory vaccination policies to inform our assessment, from psychology and behavioural science to ethics, political theory, sociology, and public policy.     

Factors Affecting Public Non-compliance With Large-scale Social Restrictions to Control COVID-19 Transmission in Greater Jakarta,Indonesia

Objectives:The Indonesian government issued large-scale social restrictions (called Pembatasan Sosial Berskala Besar, or PSBB) at the beginning of the coronavirus disease 2019 (COVID-19) pandemic to control the spread of COVID-19 in Jakarta, Bogor, Depok, Tangerang, and Bekasi (Greater Jakarta). Public compliance poses a challenge when implementing large-scale social restrictions, and various factors have contributed to public non-compliance with the regulation. This study aimed to determine the degree of non-compliance and identify the factors that contributed to public non-compliance with the PSBB in Greater Jakarta, Indonesia.Methods:This was a quantitative study with a cross-sectional design. A total of 839 residents of Greater Jakarta participated in this study. Data were collected online using a Google Form, and convenience sampling was undertaken. Univariate and multivariate analyses were performed to explore the relationships between public non-compliance with the PSBB regulation and socio-demographic variables, respondents’ opinion of the PSBB, and social capital.Results:A total of 22.6% of subjects reported participating in activities that did not comply with the PSBB. The variables that most affected non-compliance with the PSBB were age, gender, income, opinion of the PSBB, and social capital.Conclusions:Strengthening social capital and providing information about COVID-19 prevention measures, such as washing one’s hands with soap, wearing masks properly, and maintaining social distancing, is essential. Robust public understanding will foster trust and cooperation with regard to COVID-19 prevention efforts and provide a basis for mutual agreement regarding rules/penalties.     

Examining the Utility of Social Media in COVID-19 Vaccination: Unsupervised Learning of 672,133 Twitter Posts

BackgroundAlthough COVID-19 vaccines have recently become available, efforts in global mass vaccination can be hampered by the widespread issue of vaccine hesitancy.ObjectiveThe aim of this study was to use social media data to capture close-to-real-time public perspectives and sentiments regarding COVID-19 vaccines, with the intention to understand the key issues that have captured public attention, as well as the barriers and facilitators to successful COVID-19 vaccination.MethodsTwitter was searched for tweets related to “COVID-19” and “vaccine” over an 11-week period after November 18, 2020, following a press release regarding the first effective vaccine. An unsupervised machine learning approach (ie, structural topic modeling) was used to identify topics from tweets, with each topic further grouped into themes using manually conducted thematic analysis as well as guided by the theoretical framework of the COM-B (capability, opportunity, and motivation components of behavior) model. Sentiment analysis of the tweets was also performed using the rule-based machine learning model VADER (Valence Aware Dictionary and Sentiment Reasoner).ResultsTweets related to COVID-19 vaccines were posted by individuals around the world (N=672,133). Six overarching themes were identified: (1) emotional reactions related to COVID-19 vaccines (19.3%), (2) public concerns related to COVID-19 vaccines (19.6%), (3) discussions about news items related to COVID-19 vaccines (13.3%), (4) public health communications about COVID-19 vaccines (10.3%), (5) discussions about approaches to COVID-19 vaccination drives (17.1%), and (6) discussions about the distribution of COVID-19 vaccines (20.3%). Tweets with negative sentiments largely fell within the themes of emotional reactions and public concerns related to COVID-19 vaccines. Tweets related to facilitators of vaccination showed temporal variations over time, while tweets related to barriers remained largely constant throughout the study period.ConclusionsThe findings from this study may facilitate the formulation of comprehensive strategies to improve COVID-19 vaccine uptake; they highlight the key processes that require attention in the planning of COVID-19 vaccination and provide feedback on evolving barriers and facilitators in ongoing vaccination drives to allow for further policy tweaks. The findings also illustrate three key roles of social media in COVID-19 vaccination, as follows: surveillance and monitoring, a communication platform, and evaluation of government responses.     

COVID-19 Vaccine Hesitancy on Social Media: Building a Public Twitter Data Set of Antivaccine Content,Vaccine Misinformation,and Conspiracies

BackgroundFalse claims about COVID-19 vaccines can undermine public trust in ongoing vaccination campaigns, posing a threat to global public health. Misinformation originating from various sources has been spreading on the web since the beginning of the COVID-19 pandemic. Antivaccine activists have also begun to use platforms such as Twitter to promote their views. To properly understand the phenomenon of vaccine hesitancy through the lens of social media, it is of great importance to gather the relevant data.ObjectiveIn this paper, we describe a data set of Twitter posts and Twitter accounts that publicly exhibit a strong antivaccine stance. The data set is made available to the research community via our AvaxTweets data set GitHub repository. We characterize the collected accounts in terms of prominent hashtags, shared news sources, and most likely political leaning.MethodsWe started the ongoing data collection on October 18, 2020, leveraging the Twitter streaming application programming interface (API) to follow a set of specific antivaccine-related keywords. Then, we collected the historical tweets of the set of accounts that engaged in spreading antivaccination narratives between October 2020 and December 2020, leveraging the Academic Track Twitter API. The political leaning of the accounts was estimated by measuring the political bias of the media outlets they shared.ResultsWe gathered two curated Twitter data collections and made them publicly available: (1) a streaming keyword–centered data collection with more than 1.8 million tweets, and (2) a historical account–level data collection with more than 135 million tweets. The accounts engaged in the antivaccination narratives lean to the right (conservative) direction of the political spectrum. The vaccine hesitancy is fueled by misinformation originating from websites with already questionable credibility.ConclusionsThe vaccine-related misinformation on social media may exacerbate the levels of vaccine hesitancy, hampering progress toward vaccine-induced herd immunity, and could potentially increase the number of infections related to new COVID-19 variants. For these reasons, understanding vaccine hesitancy through the lens of social media is of paramount importance. Because data access is the first obstacle to attain this goal, we published a data set that can be used in studying antivaccine misinformation on social media and enable a better understanding of vaccine hesitancy.     

The COVID-19 pandemic in Ireland: An overview of the health service and economic policy response

ObjectivesTo outline the situation in Ireland with regard to the COVID-19 pandemic.MethodsAnalyse the evolution of the COVID-19 pandemic in Ireland. Review the key public health and health system responses.ResultsOver 1700 people have died with COVID-19 by July 19th while almost 3000 people had been admitted to hospital with COVID-19. A high proportion of the deaths occurred in nursing homes and other residential centres who did not receive sufficient attention during the early phase of the pandemic.ConclusionsIreland's response to the COVID-19 crisis has been comprehensive and timely. Transparency, a commitment to a relatively open data policy, the use of traditional and social media to inform the population, and the frequency of updates from the Department of Health and the Health Services Executive are all commendable and have led to a high level of compliance among the general public with the various non-medical measures introduced by the government.     

Infoveillance of the Croatian Online Media During the COVID-19 Pandemic: One-Year Longitudinal Study Using Natural Language Processing

BackgroundOnline media play an important role in public health emergencies and serve as essential communication platforms. Infoveillance of online media during the COVID-19 pandemic is an important step toward gaining a better understanding of crisis communication.ObjectiveThe goal of this study was to perform a longitudinal analysis of the COVID-19–related content on online media based on natural language processing.MethodsWe collected a data set of news articles published by Croatian online media during the first 13 months of the pandemic. First, we tested the correlations between the number of articles and the number of new daily COVID-19 cases. Second, we analyzed the content by extracting the most frequent terms and applied the Jaccard similarity coefficient. Third, we compared the occurrence of the pandemic-related terms during the two waves of the pandemic. Finally, we applied named entity recognition to extract the most frequent entities and tracked the dynamics of changes during the observation period.ResultsThe results showed no significant correlation between the number of articles and the number of new daily COVID-19 cases. Furthermore, there were high overlaps in the terminology used in all articles published during the pandemic with a slight shift in the pandemic-related terms between the first and the second waves. Finally, the findings indicate that the most influential entities have lower overlaps for the identified people and higher overlaps for locations and institutions.ConclusionsOur study shows that online media have a prompt response to the pandemic with a large number of COVID-19–related articles. There was a high overlap in the frequently used terms across the first 13 months, which may indicate the narrow focus of reporting in certain periods. However, the pandemic-related terminology is well-covered.     

Understanding Health Communication Through Google Trends and News Coverage for COVID-19: Multinational Study in Eight Countries

BackgroundDue to the COVID-19 pandemic, health information related to COVID-19 has spread across news media worldwide. Google is among the most used internet search engines, and the Google Trends tool can reflect how the public seeks COVID-19–related health information during the pandemic.ObjectiveThe aim of this study was to understand health communication through Google Trends and news coverage and to explore their relationship with prevention and control of COVID-19 at the early epidemic stage.MethodsTo achieve the study objectives, we analyzed the public’s information-seeking behaviors on Google and news media coverage on COVID-19. We collected data on COVID-19 news coverage and Google search queries from eight countries (ie, the United States, the United Kingdom, Canada, Singapore, Ireland, Australia, South Africa, and New Zealand) between January 1 and April 29, 2020. We depicted the characteristics of the COVID-19 news coverage trends over time, as well as the search query trends for the topics of COVID-19–related “diseases,” “treatments and medical resources,” “symptoms and signs,” and “public measures.” The search query trends provided the relative search volume (RSV) as an indicator to represent the popularity of a specific search term in a specific geographic area over time. Also, time-lag correlation analysis was used to further explore the relationship between search terms trends and the number of new daily cases, as well as the relationship between search terms trends and news coverage.ResultsAcross all search trends in eight countries, almost all search peaks appeared between March and April 2020, and declined in April 2020. Regarding COVID-19–related “diseases,” in most countries, the RSV of the term “coronavirus” increased earlier than that of “covid-19”; however, around April 2020, the search volume of the term “covid-19” surpassed that of “coronavirus.” Regarding the topic “treatments and medical resources,” the most and least searched terms were “mask” and “ventilator,” respectively. Regarding the topic “symptoms and signs,” “fever” and “cough” were the most searched terms. The RSV for the term “lockdown” was significantly higher than that for “social distancing” under the topic “public health measures.” In addition, when combining search trends with news coverage, there were three main patterns: (1) the pattern for Singapore, (2) the pattern for the United States, and (3) the pattern for the other countries. In the time-lag correlation analysis between the RSV for the topic “treatments and medical resources” and the number of new daily cases, the RSV for all countries except Singapore was positively correlated with new daily cases, with a maximum correlation of 0.8 for the United States. In addition, in the time-lag correlation analysis between the overall RSV for the topic “diseases” and the number of daily news items, the overall RSV was positively correlated with the number of daily news items, the maximum correlation coefficient was more than 0.8, and the search behavior occurred 0 to 17 days earlier than the news coverage.ConclusionsOur findings revealed public interest in masks, disease control, and public measures, and revealed the potential value of Google Trends in the face of the emergence of new infectious diseases. Also, Google Trends combined with news media can achieve more efficient health communication. Therefore, both news media and Google Trends can contribute to the early prevention and control of epidemics.