key: cord-0560574-kdnu68p5
authors: Tsao, Shu-Feng; Chen, Helen; Tisseverasinghe, Therese; Yang, Yang; Li, Lianghua; Butt, Zahid A.
title: What social media told about us in the time of COVID-19: a scoping review
date: 2021-01-05
journal: nan
DOI: nan
sha: b561ef2d626ab2c55d1deac7b141b068c570146b
doc_id: 560574
cord_uid: kdnu68p5

With the onset of COVID-19 pandemic, social media has rapidly become a crucial communication tool for information generation, dissemination, and consumption. In this scoping review, we selected and examined peer-reviewed empirical studies relating to COVID-19 and social media during the first outbreak starting in November 2019 until May 2020. From an analysis of 81 studies, we identified five overarching public health themes concerning the role of online social platforms and COVID-19. These themes focused on: (i) surveying public attitudes, (ii) identifying infodemics, (iii) assessing mental health, (iv) detecting or predicting COVID-19 cases, (v) analyzing government responses to the pandemic, and (vi) evaluating quality of health information in prevention education videos. Furthermore, our review highlights the paucity of studies on the application of machine learning on social media data related to COVID-19 and a lack of studies documenting real-time surveillance developed with social media data on COVID-19. For COVID-19, social media can play a crucial role in disseminating health information as well as tackling infodemics and misinformation.

The Corona Virus Disease 2019 (COVID- 19) , or Severe Acute Respiratory Syndrome Novel Coronavirus (SARS-COV-2), is a significant international public health issue. As of November 14, 2020, an estimated 53.5 million people around the world have been infected with the virus, with about 1.3 million deaths. 1 As a consequence of the pandemic, social media is becoming the platform of choice for public opinions, perceptions, and attitudes towards various events or public health policies regarding COVID-19. 2 Social media has become an even more pivotal communication tool for governments, organizations, and universities to disseminate crucial information to the public. Numerous studies have already used social media data to help identify and detect infectious disease outbreaks and to interpret public attitudes, behaviours, and perceptions. 3-6 Social media, particularly Twitter, may be used to explore multiple facets of public health research. A recent systematic review identified six categories of Twitter use for health research, namely, content analysis, surveillance, engagement, recruitment, interventions, and frequency analysis of Twitter users. 4 However, this review only included broader research terms such as health, medicine, or disease using Twitter data and did not focus on specific disease topics such as COVID- 19 . Another article analyzed tweets on COVID-19 and identified 12 topics categorized into four main themes: the origin of COVID-19, the source of the coronavirus, impact of COVID-19 on individuals, and countries, and methods of decreasing the spread of In this study data were not available on COVID-19 related tweets before February 2020 thereby missing the initial part of the epidemic, and the data on tweets were limited to Twitter only within a one-month period.

Social media can also be effectively used to communicate health information to the general public during a pandemic. Emerging infectious diseases (EID), such as COVID-19, almost always results in increased usage and consumption of media of all forms by the general public for information. 8 Therefore, social media plays a vital role in people's perception of disease exposure, resultant decision making and risk behaviours. 9,10 As information on social media is generated by users, such information can be subjective or inaccurate, and frequently includes rumours, misinformation, and conspiracy theories. 11 Hence, it is imperative that accurate and timely information is disseminated to the general public about emerging threats such as the novel coronavirus. A systematic review explored the major approaches used in published research on social media and EID . 12 The review identified three major approaches: (a) assessment of the public's interest in and responses to EID, (b) examination of organizations' use of social media in communicating EID, and (c) evaluation of the accuracy of EID related medical information on social media. However, this review did not focus on studies that used social media data to track and predict EID outbreaks.

Analyzing and disseminating information from peer-reviewed published research can guide policymakers and public health agencies to design interventions for accurate and timely knowledge translation to the general public. Therefore, keeping in view the gaps mentioned above, we conducted a scoping review with the aim of understanding what roles social media have been playing since the beginning of the COVID-19 crisis. We wanted to know specifically about public attitudes and perceptions towards COVID-19 on social media, information about COVID-19 on social media, social media use for prediction and detection of COVID-19, the impact of COVID-19 on mental health and government responses to COVID-19 on social media. Our objective was to identify studies on social media related to COVID-19 that focused on the following: (1) Infodemics (2) Public attitudes, (3) Mental health, (4) Detection or prediction of COVID-19 cases, (5) Government responses to the pandemic, and (6) Quality of health information in videos.

Studies exploring the use of social media on COVID-19 were reviewed using the scoping review methodology of Arksey and O'Malley 13 and Levac et al. 14 The five-step scoping review protocol was followed, and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) extension for scoping reviews was employed in this study.

Exploratory searches were conducted on COVID-19 Open Research Dataset Challenge as well as Google Scholar in April 2020. These searches helped define the review scope, develop the research questions, and determine eligibility criteria. After such activity, MEDLINE/PubMed, Scopus, and PsycINFO were selected for this review since they include peer-reviewed literature in the fields of medicine, behavioural sciences, psychology, healthcare system, and clinical sciences. Since the start of the current pandemic, COVID-19 articles were reviewed and published at an unprecedently rapid rate with numerous publications that were peer-reviewed and available ahead of print, referred to as "pre-prints" or "articles in press." In this review, we consider peer-reviewed pre-prints equivalent to normal peer-reviewed articles, and relevant articles were screened accordingly.

The search strategies utilized index terms, where applicable, and free-text terms to capture the following two concepts: (a) social media, including both general terms and specific platform names (e.g., Twitter, Facebook, Weibo, YouTube); (b) COVID-19. For each database, both indexed terms (i.e., MeSH, Emtree) and natural language keywords were used with Boolean operators (i.e., AND, OR, NOT) and truncations. Table 1 shows keywords, indexed terms, and their variations used for the literature search. Since each database has distinctive search functionality, individually tailored search statements were developed with appropriate search filters for each database. Final search statements, along with a list of search results, were downloaded from each database. Articles were included if (a) they discussed the use of social media for COVID-19 research, and (b) were original, empirical studies. Only peer-reviewed articles, including peer-reviewed preprints, in English or Chinese, were included. A decision to include Chinese publications was based on the fact that COVID-19 cases were first reported in Wuhan, China and many initial and relevant studies were published in Chinese; therefore, we wanted to capture a majority of studies that were on the use of social media for COVID-19 research. All articles published between November 1, 2019, and November 4, 2020, were included. Publications such as reviews, opinion pieces, pre-prints, books, book chapters and any empirical study that were not peer-reviewed or written in languages other than English or Chinese were automatically excluded. The final reference list was generated based on originality and relevance to the broad scope of this review.

Primary reviewer (SFT) mainly carried out title and abstract screening for each article to determine if an article met the inclusion criteria. If the criteria were confirmed, then it was included; otherwise, it was excluded. Articles were broken into paragraphs to contain one code in each section. Next, quotes were sorted under each code, applying Ose's method. 15 Braun and Clark's thematic analysis method was employed and involved searching for the text that matched the identified predictors (codes) in from the quantitative analysis and discovering 

According to the World Health Organization (WHO), the term "infodemic," a combination of "information" and "epidemic," refers to a fast and widespread of both accurate and inaccurate information about something, such as a disease like COVID-19. 18 as global interest of COVID-19 information increased, so did its infodemic. 19 Gallotti et al analyzed over 100 million tweets and found that even before the onset of the COVID-19 pandemic, entire Inlay's expose measurable waves of infodemic has threatened public health. 20 Pulido et al sampled and

analyzed 942 tweets which revealed that while false information had higher number of tweets, it also had less retweets and lower engagement than tweets comprising scientific evidence or factual statements. 21 Facebook posts in Australia related to such conspiracy from 1 January to 12 April 2020 using time series and network analysis. 27 The results showed that this conspiracy went viral after and demonstrated misinformation was mainly driven by rumors, stigma, and conspiracy theories circulating on various social media and other online platforms. 28 Associations between infodemic and bot activities on social media is another important research direction. One study analyzed 12 million tweets from the US and 15 million tweets from the Philippines from March 5 to 19, 2020 and both countries showed a positive relation between bot activities and rate of hate speech in denser and more isolated communities. 29 Brennen et al qualitatively analyzed 96 samples of visuals from January through March 2020 and categorized misinformation into 6 trends, and found, fortunately, there have been no involvement of AI "deepfakes" techniques so far. 30 

Three themes emerged under this category: public attitudes, mental health, and detection or prediction of COVID-19 cases. The former two themes are reflections regarding the public perceptions and mental health impacts of the pandemic; the latter includes typical surveillance studies aiming to propose ways for the detection or prediction of the COVID-19 cases. Reddit comments showed that "Symptoms" accounted for 27% of all comments, followed by "Prevention" (25%). 32 Likewise, another content analysis of 155,353 unique English tweets show the most mentioned topic was "peril of COVID-19." 33 Still another research examined 126,049 English tweets using sentiment analysis and latent Dirichlet analysis (LDA) for topic modeling revealed the most common emotion mentioned was fear, and the most common topic mentioned was the economic and political effects. 34 Al-Rawi et al studied emojis in over 50 million tweets and identified 5 primary subjects: "morbidity fears," "health concerns,"

"employment and financial issues," "praise for frontline workers," and "unique gendered emoji use." 35 Samuel et al investigated 293,597 tweets with sentiment analysis and found more positive emotions toward the US economy re-opening than negative emotions. 36 Analyzing 2,558,474 English tweets using clustering and network analyses, Odlum et al found African

Americans shared positive sentiments and encouraged virtual discussions and prevention behaviours. 37 A study investigated gender differences in terms of topics by analyzing 3,038,026

English tweets. 38 The results demonstrated that tweets from females were more likely about family, social distancing, and healthcare, whereas tweets from males were more about sports cancellations, pandemic severity, and politics. 46 One thematic analysis study of 1,920,593 Arabic tweets in Egypt revealed that negative emotions and sadness were loaded in tweets showing affective discussions, and the dominant themes included "the outbreak of the pandemic," "metaphysics responses," "signs and symptoms in confirmed cases," and "conspiracism." 47 In Singapore, Lwin et al examined 20,325,929 tweets using sentiment analysis and showed the public emotions shifted over time:

from fear to anger and from sadness to grateful. 48 Chang et al examined over 1.07 million Chinese texts from various online sources in Taiwan using deductive analysis and found negative sentiments mainly came from online news with stigmatizing languages linked with the COVID-19 pandemic. 49 In India, one study investigated 410,643 tweets via sentiment analysis and LDA and showed that positive emotions were overall significantly higher than negative sentiments, but this observation diminished at individual levels. 50 Another research analyzed 29,554 tweets from lockdown 2.0 and 47,672 tweets from lockdown 3.0 via sentiment analysis uncovered positive attitudes toward lockdown 2.0 but turned to negative attitudes toward lockdown 3.0 in India. 51 One study analyzed 868 posts from Reddit and found sentiments to be 50% neutral, 22% positive, and 28% negative in India. 52 A study in South Korea examined 43,832 unique users and their relations on Twitter using content and network analyses demonstrated tweets including medical news were more popular. 53 A research from Ireland analyzed 203,756 tweets through topic modeling and identified that "WAR" was the most common used frame. 54 In the USA, Damiano et al qualitatively analyzed 600 English tweets and showed neutral sentiment. Politics also played an essential role in shaping people's opinion. 55 A research studied 19,803 tweets from Democrats and 11,084 tweets from Republicans using RF in the USA and showed that Democrats emphasized more on public health threats to American workers, whereas Republicans emphasized more threats from China and business. 56 Results of a study involved various online data sources from Italy, UK, USA, and Canada showed media was the major driver of the public's attention, despite rapid saturation. 57 Compared with other users, Reddit users focused more on disease data and prevention interventions. Researchers in Spain studied 22,223 tweets using topic modeling and network analysis. 58 They identified eight frames and found that the entire pandemic could be divided into three periods: "pre-crisis," "lockdown" and "recovery" periods. On the other hand.

Jelodar et al proposed a novel method to detect meaningful latent-topics and sentimentcomment-classification on COVID-19-related posts using 563,079 COVID-19-related English Reddit posts. 59 Samuel et al examined over 900,000 tweets to study the accuracy of tweet classifications among logistic regression and Naïve Bayes (NB) methods. They found that NB had 91% of accuracy compared with 74% from the logistic regression model. 60 Another study investigated how the Chinese government used the popular social media platform, Sina Weibo, to promote citizen engagement during the COVID-19 crisis. 61 Xuehua

Han et al. 62 analyzed 1,413,297 Weibo posts and found that the public reacted sensitively to the epidemic, especially in metro areas. 62 concerns: "the virus origin," "symptom," "production activity," and "public health control" in China. 66 Xi et al examined 241 topics with their views and comments via thematic and temporal analysis and found the "Contributing to the community" theme was the most dominant in the first phase (January 20-February 20, 2020). 67 The "Older patients in hospitals" theme was most Tweets from Italy. 68 The findings showed Italians paid more attention to leisure whereas Google Trends, and numbers of COVID-19 cases and deaths. 70 Daily GT was correlated to 7

indicators, whereas daily BAI was only correlated to 3 indicators. 71 English tweets from pre-introduction period and 177,327 tweets from post-introduction period. 73 The results showed an almost 10 times increase nationwide and statewide from 0.38 tweets referencing those two terms posted per 10,000 people in the pre-period to 4.08 tweets post-period in the USA. Another similar study examined 339,063 tweets from non-Asian respondents via local polynomial regression and interrupted time-series analysis. 74 The findings demonstrated that when stigmatizing terms like "Chinese virus" were used by media starting from March 8, 2020, the bias index-Implicit Americanness Bias-began to increase, and such bias was more profound in conservatives. Nguyen 

Two of the 81 reviewed studies, both based in China, focused on assessing social media users' mental health. 78, 79 A cross-sectional study 78 investigated the relationship between anxiety and social media exposure (SME), theoretically defined as "the extent to which audience members have encountered specific messages". 80 

Six of the 81 studies investigated the prediction of COVID-19 outbreaks using social media data. Qin et al. 79 attempted to predict the number of new suspected or confirmed COVID-19

cases by collecting social media search indexes (SMSI) for symptoms such as dry cough, fever, chest distress, coronavirus, and pneumonia. The data were analyzed using subset selection, forward selection, lasso regression, ridge regression, and elastic net. Results showed the optimal model was constructed via the subset selection. The lagged SMSI was a predictor of new suspected COVID-19 cases and could be detected 6-9 days before the new case confirmation 77 . To evaluate the possibility of early prediction of the COVID-19 cases via internet searches and social media data, Li et al. 82 used keywords "coronavirus" and "pneumonia" to retrieve corresponding trend data from Google Trends, Baidu Index, and Sina Weibo Index. Using the lag correlation, the results showed that for new lab-confirmed cases, the highest correlation was found 9-12 days earlier for the keyword "coronavirus" in the three platforms. Similarly, for the new suspected cases, the highest correlation was found 6-8 days earlier for "coronavirus." The keyword "pneumonia" for new suspected cases had the highest correlation eight days earlier across the three platforms. 82 Peng et al studied 1,200 Weibo records using spatiotemporal analysis, kernel density analysis, and ordinary least square regression and found that "scattered infection", "community spread", and "full-scale outbreak"

were three phases of early COVID-19 transmission in Wuhan, China. 83 Social Media as Disease Control

To make the public inoculated against misinformation, public health organizations should create and spread accurate information on social media because social media have increasingly played an important role in policy announcement and health education. Six of the 81 articles were categorized as "government responses" because they examined how government messages and health education material were generated and consumed on social media platforms. Two studies analyzed data from Sina Weibo, 86, 88 and the other four studies analyzed data from Twitter. 88, 89 Zhu et al. 86 pandemic by collecting 203 tweets. 88 The findings showed that 82.8% were "informative,"

28.6% of them linked to official government resources; 9.4% were "morale-boosting;" and 6.9% were "political". 88 They categorized 16 types of messages and identified inconsistent and incongruent messages expressed in 4 critical prevention topics: mask wearing, risk assessments, stay at home order, and disinfectants/sanitizers. information from these videos. 99 The other one, however, uncovered poor quality for 24 out of 55 (43.6%) English videos, whereas good quality accounted for only 2 (3.6%) videos. 100

Studies on social media data revealed our attitudes and mental state to a certain extent during the COVID-19 crisis. These studies also showed how we generated, consumed and propagated information on social media platforms when facing the rapid spread of the novel coronavirus and extraordinary measures for the containment. In our review, public attitudes accounted for nearly 60% of the reviewed articles. In terms of social media platforms, 46% of the chosen articles used data from Twitter, followed by Weibo (18%). Machine learning analyses, such as LDA and RF, were applied in research that studied public attitudes.

We identified six themes based on our modified SPHERE framework, including (i) infodemics, (ii) public attitudes (iii) mental health, (iv) detection or prediction of COVID-19 cases, (v) government responses to the pandemic, and (vi) quality of prevention education videos.

However, a common limitation in all chosen studies using social media data is the "data quality" challenge, such as various formats, metrics, or even the definition of a variable. For instance, the definition of a "view" on one social media platform is likely different from Additionally, a significant proportion of the studies were done using Sina Weibo, which, although used by a large number of people, is exclusive of China, which may lead to an overrepresentation of a single country in the scoping review.

In summary, although our scoping review has limitations embedded from the chosen studies, it recognized six themes that have been studied so far, and future research directions are also identified. Our adopted framework can serve as a fundamental and flexible guideline when conducting studies related to social media and epidemiology. 

The authors declared no conflicts of interest

There was no funding for this review. • Content analysis • "Symptoms" had highest number of posts (27%), followed by "Prevention" posts (25%) • "Furthering discussion" was the most common intent in "Symptoms" posts (28% 

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