Background: The COVID-19 pandemic exacerbated existing racial/ethnic health disparities in the United States. Monitoring nationwide Twitter conversations about COVID-19 and race/ethnicity could shed light on the impact of the pandemic on racial/ethnic minorities and help address health disparities.
Objective: This paper aims to examine the association between COVID-19 tweet volume and COVID-19 cases and deaths, stratified by race/ethnicity, in the early onset of the pandemic.
Methods: This cross-sectional study used geotagged COVID-19 tweets from within the United States posted in April 2020 on Twitter to examine the association between tweet volume, COVID-19 surveillance data (total cases and deaths in April), and population size. The studied time frame was limited to April 2020 because April was the earliest month when COVID-19 surveillance data on racial/ethnic groups were collected. Racially/ethnically stratified tweets were extracted using racial/ethnic group–related keywords (Asian, Black, Latino, and White) from COVID-19 tweets. Racially/ethnically stratified tweets, COVID-19 cases, and COVID-19 deaths were mapped to reveal their spatial distribution patterns. An ordinary least squares (OLS) regression model was applied to each stratified dataset.
Results: The racially/ethnically stratified tweet volume was associated with surveillance data. Specifically, an increase of 1 Asian tweet was correlated with 288 Asian cases (P<.001) and 93.4 Asian deaths (P<.001); an increase of 1 Black tweet was linked to 47.6 Black deaths (P<.001); an increase of 1 Latino tweet was linked to 719 Latino deaths (P<.001); and an increase of 1 White tweet was linked to 60.2 White deaths (P<.001).
Conclusions: Using racially/ethnically stratified Twitter data as a surveillance indicator could inform epidemiologic trends to help estimate future surges of COVID-19 cases and potential future outbreaks of a pandemic among racial/ethnic groups.
As the novel SARS-CoV-2 virus, which causes COVID-19, started spreading worldwide in early 2020, people began practicing social distancing as a measure to reduce contagion . The public also started using Twitter to exchange information about the pandemic, which contributed to the exponential increase in social media traffic volume [ ]. According to the Centers for Disease Control and Prevention (CDC), as of March 31, 2022, the pandemic had claimed over 995,000 lives in the United States, which continues to rise [ , ]. This is the worst pandemic the United States has experienced since the 1918 flu [ ].
Racial/ethnic disparities in health in the United States have been well documented. Stark racial disparities exist in health outcome measures, health care, and chronic health conditions [, ]. In almost all health outcome measures, these disparities are precipitated by the disproportionate representation of Black Americans, Hispanic/Latino Americans, Asian Americans, and other racial/ethnic minorities [ , ] and often due to limited access these communities have to hospitals and health care facilities [ ]. The COVID-19 crisis has further exacerbated those disparities because racial/ethnic minorities have higher representation in sectors most affected by the pandemic and are more likely to be employed in low-wage or precarious jobs [ , , ].
An increasing number of public health and medical agencies are using Twitter data to monitor disease trends and detect outbreaks at the national and local levels [, ] (ie, dental pain [ ], cancer [ ], as well as a syndromic surveillance system [ ]). Twitter data, especially geotagged tweets, have several advantages over traditional surveillance data. Traditional surveillance systems, such as surveys or clinical data collection, are expensive and time-consuming monitoring mechanisms [ ]. Tweets, on the other hand, are more timely and less costly [ , ]. In addition, the geographic extent of tweet data makes it easy to conduct multiscale studies, from a neighborhood to counties to across multiple nations [ ]. Geotagged tweets can also be geocoded to match exact location information, making it possible to link to other data sources based on common geographical attributes (ie, state, county, or local address) for geospatial analysis [ ]. By contrast, most publicly available traditional surveillance data have restricted geographic variables, and county level is often the finest spatial resolution as these data need to be aggregated from a privacy perspective [ - ].
In 2020, Twitter data alone was used for the national surveillance of COVID-19 hospitalizations in Belgium , COVID-19–related anti-Asian sentiments in the United States [ ], evaluating world leaders’ COVID-19 response measures [ ], and tracking mental health symptoms mentioned during the COVID-19 pandemic within the United States [ ]. In particular, the relation between tweet volume and surveillance was explored for various purposes [ - ]. Increased tweet volume is often assumed to correlate with increased public interest in certain topics. Subsequently, this approach was used as a measure to monitor public discourse on mask-wearing [ ], increased COVID-19 cases [ ], and predicting outbreaks [ , ].
Despite these great benefits, Pobiruchin et al  pointed out the need and importance of investigating the potential correlation between tweet volume and infection or death rates; Nguyen et al [ ] suggested future work on the geographical variation in area-level COVID-19 infection and mortality rates and their associations with demographics such as density of racial/ethnic groups. Considering the research gaps and the context of the COVID-19 pandemic’s disproportionate impact on racial/ethnic minorities, our goal was to explore the association between racially/ethnically stratified tweets and COVID-19 cases and deaths. We believe this study will reveal the potential relation of public discourse about the pandemic’s impact on each race/ethnicity group and COVID-19 cases and deaths. Using racially/ethnically stratified tweet volume as a surveillance indicator will provide more evidence and help address racial/ethnic health disparity in this COVID-19 crisis.
English tweets geotagged within the United States from April 1, 2020, to April 30, 2020, were downloaded using the Tweepy Python library—Twitter’s search application programming interface (API) —and a set of predefined search terms . Referencing the most searched terms from Google’s Daily Search Trends summary from March 10, 2020, to April 1, 2020, we derived a list of search terms. The search terms included (1) the most widely adopted scientific name of the disease (“corona,” “COVID-19,” “pandemic,” “coronavirus”), (2) the name of the racial/ethnic groups in which we were interested (“Asian,” “Blacks,” “Hispanics,” and “Latino”), and (3) other related terms (“test positive,” “COVID,” “n95,” “Flatten the curve,” “Social Distancing,” “Chinese Virus”; see the complete list of keywords in ). We only included geotagged tweets with location information (ie, geographical coordinates and city names) to filter tweets within the United States. The location information allowed us to match tweets to specific states and then link state-level surveillance data for further analysis. Each tweet’s text, timestamp, coordinates, or place names were extracted and used in the detailed analysis.
COVID-19 cases were the “total number of confirmed plus probable cases of COVID-19 reported by the state or territory” for April 2020 obtained from The COVID Tracking Project . COVID-19 deaths were “total fatalities with confirmed or probable COVID-19 case diagnosis” from The COVID Tracking Project, the only source with data about race across all states. These data cannot be found in neither the CDC COVID Data Tracker nor the COVID-19 Dashboard at John Hopkins University. State-level population data for each racial/ethnic group were obtained from the US Census Bureau’s 2019 American Community Survey 5-year estimates [ ].
Spatial and Statistical Analysis
illustrates the organization of the tweets and COVID-19 case and death data and the analytics implemented to examine the racial/ethnic relationships. First, the tweets were cleaned using natural language processing steps, removing punctuation, English stop words (ie, a, an, the), and special characters. Using the name of each racial/ethnic group, we extracted tweets that contained conversations about the subpopulations of interest: Asians, Blacks, Latinos, and Whites. If multiple racial/ethnic groups were mentioned in 1 tweet, the tweet was examined multiple times. “Black” and “White” can also refer to the name of colors. To exclude tweets that used “Black” and “White” but did not mean ethnic groups, the tweets were manually evaluated to identify and exclude irrelevant tweets. Next, we calculated the total number of tweets (or tweet volume) for Asians, Blacks, Latinos, and Whites in 50 states and Washington, DC, based on geotagged information.
COVID-19 case and death data were recorded twice a week, so we combined these data to yield the total cases and deaths for April 2020. To map and compare the cases and deaths across racial/ethnic groups, we used 2019 US Census population data to adjust the number of cases or deaths per 100,000 Asians/Blacks/Latinos/Whites. The population-adjusted cases and deaths were mapped using the geospatial processing tool ArcPro 2.5 (ESRI, 2020; Redland, CA) to allow comparison across racial/ethnic groups.
Using the state name as the common field, we linked tweet volume, COVID-19 cases and deaths, and population estimates for the 50 states and Washington, DC. Four ordinary least squares (OLS) regressions were implemented for Asians, Blacks, Latinos, and Whites, respectively, to examine the association of tweet volume with COVID-19 cases. In each model, tweet volume was used as the independent variable, the population was the control variable, and the number of COVID-19 cases was the outcome variable. Another 4 OLS regressions were implemented for Asians, Blacks, Latinos, and Whites, respectively, to examine the association between tweet volume and COVID-19 deaths. Tweet volume, population, and COVID-19 deaths were used as each model’s independent, control, and outcome variables, respectively.
The final analysis included 189,946 geotagged tweets. Of these, we extracted 141 (0.07%) tweets mentioning Asians, 869 (0.46%) tweets about Blacks, 47 (0.02%) tweets about Latinos, and 588 (0.31%) tweets about Whites. Example tweets about each racial/ethnic group are presented in.
The spatial locations of tweets were mapped, and hot spots based on tweet volume were identified (). These hot spots (gradient from red to yellow indicates increasing volume) include New York, Los Angeles, and Chicago. The non-hot spot locations (blue-gray color) represent low tweet volume.
The distributions of population-adjusted COVID-19 cases for Asians, Blacks, Latinos, and Whites in April 2020 are shown in. Alaska and Hawaii are not shown on the maps because the cases were few for Asians and close to 0 for the other 3 racial/ethnic groups in both states in April 2020. The number of COVID-19 cases (≤95,345 per 100,000) among Asians was highest in Illinois, Massachusetts, and Washington. Similarly, Latino cases were highest in Illinois, Utah, and Washington. The highest cases among Blacks were in the Midwest, Northeast, and Southern regions (eg, Illinois, Massachusetts, Alabama, among others). Compared with Asians and Blacks, the number of COVID-19 cases among Whites was the lowest across all states.
shows the distributions of population-adjusted COVID-19 deaths for Asians, Blacks, Latinos, and Whites in each state for April 2020. The number of deaths among Asians was highest (>220 per 100,000) in Illinois, Massachusetts, New York, and Washington. Massachusetts, New York, and Washington were the states that reported the highest numbers of death for Latinos (>220 per 100,000). Similar to the number of infected cases, the number of deaths among Blacks was also highest (>220 per 100,000) in the Midwest, Northeast, and Southern regions (ie, Alabama, Illinois, Massachusetts, Michigan, New Jersey, New York, North Carolina, South Carolina, and Texas).
and show the linear regression results between COVID-19 cases and tweet volume. We observed a significant relationship between Asian tweet volume and the number of COVID-19 cases. Specifically, an increase of 1 Asian tweet was linked to 288 Asian cases (Model 1, adjusted R2=0.72, P<.001). The rise of Black, Latino, and White tweets was not correlated with COVID-19 cases. Furthermore, tweet volume for all racial/ethnic groups was significantly associated with COVID-19 deaths: An increase of 1 Asian tweet was linked to 93.4 Asian deaths (Model 5, adjusted R2=0.57, P<.001); an increase of 1 Black tweet was linked to 47.6 Black deaths (Model 6, adjusted R2=0.23, P<.001); an increase of 1 Latino tweet was linked to 719 Latino deaths (Model 7, adjusted R2=0.38, P<.001); and an increase of 1 White tweet was linked 60.2 White deaths (Model 8, adjusted R2=0.18, P<.001).
|Ethnic group||Example tweets|
|Model, outcome, and measurement variable||Coefficient||P value||Confidence interval||Adjusted R2|
|Model 1: Asian COVID-19 cases|
|Asian tweet volume||288.192||0.00||162.3 to 414.0||0.72|
|Asian population||0.002||0.00||0 to 0|
|Model 2: Black COVID-19 cases|
|Black tweet volume||97.088||0.28||–80.3 to 274.5||0.08|
|Black population||0.004||0.07||0 to 0|
|Model 3: Latino COVID-19 cases|
|Latino tweet volume||2161.828||0.05||–24.0 to 4347.7||0.26|
|Latino population||0.001||0.00||0 to 0|
|Model 4: White COVID-19 cases|
|White tweet volume||163.938||0.07||–12.3 to 340.1||0.19|
|White population||0.001||0.03||0 to 0|
|Model, outcome, and measurement variable||Coefficient||P value||Confidence interval||Adjusted R2|
|Model 5: Asian COVID-19 deaths|
|Asian tweet volume||93.3750||0.00||68.9 to 117.7||0.57|
|Asian population||–0.0001||0.02||0 to 0|
|Model 6: Black COVID-19 deaths|
|Black tweet volume||47.6001||0.00||24.1 to 71.0||0.23|
|Black population||–0.0002||0.39||0 to 0|
|Model 7: Latino COVID-19 deaths|
|Latino tweet volume||719.2882||0.00||463.4 to 975.1||0.38|
|Latino population||–0.0001||0.29||0 to 0|
|Model 8: White COVID-19 deaths|
|White tweet volume||60.2326||0.00||26.1 to 94.3||0.18|
|White population||–0.0002||0.07||0 to 0|
To our knowledge, this is one of the first studies to investigate the association between racially/ethnically (Asian, Black, Latino, and White) stratified COVID-19 tweet volume and COVID-19 surveillance data (cases and deaths). Our results revealed a relationship between tweet volume and COVID-19 cases and deaths for all racial/ethnic groups in our study during April 2020 despite varying degrees of association. The key contribution of our study is the examination of the racial and ethnic bias of the pandemic using early-onset data from social media and reported cases and deaths, which was not evident early during the pandemic. This study demonstrates the importance of timely surveillance data collection to characterize the threat and spread of infectious disease among certain racial/ethnic communities, which is critical to effectively and efficiently guide a well-coordinated and targeted public health response to reduce the spreading and adverse health impacts of a pandemic.
The concentration of high tweet volumes in several hot spot locations, including New York, Los Angeles, and other places in April, could be influenced by 2 main factors. First, the regions have a large population and densely populated urban areas and a significant number of active Twitter users. Second, the increasing number of COVID-19 cases and deaths in these regions could potentially cause the COVID-19-related tweet volume to spike. The regression analysis showed that Asian tweet volume and COVID-19 cases correlated with their population. However, this association was not observed among other racial/ethnic groups. One possible reason could be the alarming increase in discrimination and hate crimes against Asians as the pandemic surged. A similar study by Nguyen et al  investigating negative sentiments using tweets before and shortly after COVID-19 emergence found an abnormally higher proportion of negative tweets referencing Asians than tweets about other racial/ethnic groups. The OLS results also revealed that the Asian tweet volume and Asian population were associated with Asian deaths, whereas the volume of Black, Latino, and White COVID-19 tweets was only associated with COVID-19 deaths. These different associations with tweet volume between COVID-19 cases and deaths could partially be because of restricted testing capacities for COVID-19 cases as opposed to deaths, which were actively reported and recorded. Thus, COVID-19 cases may be underestimated, and an association with COVID tweet volumes was not detected.
The findings also suggest that Twitter data, when stratified by racial/ethnic groups, could yield novel insights in using social media to detect trends in disease occurrence and the potential impact on specific racial/ethnic groups. A previous study investigated changes in racial sentiment before and after COVID-19 emergence using race-related tweets (Asian, Black, Latino, White) . The study found a higher proportion of negative tweets mentioning Asians increased by more than 68%, while those relating to other racial/ethnic minorities and Whites remained stable. The timely use of tweet data is essential to detect area-level racial sentiment [ ]. One side note about this previous study is that only the proportion of tweets about each race was compared and area-level comparisons were not explored; however, our research conducted area-level analysis and explored the association between tweet volume and surveillance data. Another study investigated the association between the volume of tweets against mask wearing and daily COVID-19 cases and found a rise in negative tweet volume was strongly correlated with the number of new cases [ ]. This finding concurs with ours regarding the change in tweet volume associated with COVID-19 cases and deaths, although at varying degrees for different racial/ethnic groups. Different from our research, this study did not use geotagged tweets. Thus, no geographic attributes can be used to link population density, which is also a key factor influencing tweet volume. Cuomo et al [ ] investigated subnational longitudinal and geospatial trends of COVID-19 in the United States between March 3, 2020, and April 13, 2020. The authors explored the association between change in tweet volume and population-adjusted COVID-19 case increase. They also conducted geospatial hot spot analysis and included population-adjusted results and found a high proportion of rural inhabitants in some of the hot spot regions, which partially overlaps with our results, as we also found high COVID-19 cases per 100,000 people in some rural states (ie, Alabama, Iowa) for the Black population.
Although previous studies have examined the relationship between tweets and COVID-19 cases, none included COVID-19 deaths as a measure in their investigations. COVID-19 deaths are an important measure because our statistical results show that all Asian, Black, Latino, and White COVID-19 tweets were associated with COVID-19 deaths but not with COVID-19 cases. One possible explanation for this is that the reporting of COVID-19 cases was limited by testing capacity in April 2020, but this limitation influenced the reporting of COVID-19 deaths less.
Future research could focus on exploring the use of Twitter to develop streamlined tools to automatically extract, process, and analyze COVID-19 (or other public health events) and racially/ethnically related tweets to monitor tweet volume about racial/ethnic groups and COVID-19–related health disparities . The state-level COVID-19 surveillance data that we used limited our geospatial analysis to the state level, despite the much finer spatial resolution of the geotagged tweets. Although state-level aggregation is essential from a privacy perspective, in the future, we will consider using surveillance data at a finer spatial resolution (eg, county) to explore the variability of infected cases and deaths among different demographic groups (based on factors such as race, gender, income, age). Using early-onset Twitter data, we demonstrated a pandemic's racial/ethnic disparity among the most populous racial/ethnic groups, which could be replicated in future public health events using social media data. In the future, we will expand this study by including surveillance data for other racial/ethnic groups if this information is available.
Public Health Implications
This retrospective study provides evidence for the use of racially/ethnically stratified Twitter data as a surveillance indicator. Social media data can provide insights to track epidemiological trends, especially for outbreaks, epidemics, or pandemics that are novel and spread fast. Such findings can aid public health practitioners and policy makers make public health decisions using this nontraditional near-real-time data set before traditional surveillance data sets are available. The research findings also encourage health care professionals to actively engage in public discourse to present scientific and clinical evidence to help reduce racial/ethnic health disparities [, ] and eliminate misinformation. For instance, COVID-19–induced discrimination against Asians is mainly due to misunderstanding and politicizing COVID-19 origins; health care professionals can share scientific findings of possible COVID-19 origins that might not have been widely circulated [ ].
The association between tweet volume and COVID-19 cases and deaths proved that tweet volume could be used as proxy surveillance data to estimate the spread of COVID-19 cases and deaths. This association can also evaluate potential locations for future COVID-19 cases and deaths. Identifying future areas for COVID-19 cases and deaths could be used for public health responses when official surveillance is not available and complement official surveillance data when available. Since Twitter surveillance is low-cost and efficient, it can be streamlined and implemented as a long-term disease surveillance tool to ensure prompt response to future public health crises.
Although this study used COVID-19 cases and deaths as the 2 outcomes of this disease, we are aware of the concerns about the accuracy of cases due to limited testing capabilities (ie, lack of access to testing) and challenges in the attribution of the cause of death in the early onset of the coronavirus outbreak . Considering these factors, the authors acknowledge the limitation of using the counts of COVID-19 cases and deaths as variables in our models. Similar to other studies that rely on social media data, Twitter users are not representative of the population (ie, one-third of its users are between 25 and 34 years old), thus bringing selection bias to the analysis [ , ]. The exclusion of tweets in other languages, especially Spanish, also limits our understanding of information exchange between Latinos who speak Spanish.
Another limitation of this study is the spatial granularity at the state level because the surveillance data were obtained at the state level. If surveillance data can be obtained at a finer spatial resolution, then the analysis will yield more accurate and precise results and insights into the location of COVID-19 cases and deaths. Although this is a limitation of the study, given the bias towards specific racial/ethnic groups, and in order to protect the privacy of the users, state-level aggregation is a good starting point to demonstrate the methodology implemented herein to explore the racial/ethnic aspect of a pandemic.
We only used tweets collected in April 2020 and conducted a cross-sectional analysis. We acknowledge that this analysis could offer more insights into the temporal trends of conversations about different racial/ethnic groups if the data set had a larger temporal scale. However, several issues exist with extended time frame data. First, many states did not report or minimally reported race data, as it was not a requirement to report COVID-19 cases and deaths after several months into the pandemic . Second, some states intentionally adjusted or manipulated their case and death data to obscure rising infection rates and mislead the public to achieve specific political goals, such as Florida's COVID-19 dashboard incident [ - ]. Since 2020, different states have implemented varying policies about social distancing and wearing masks, which probably contributed to asymptomatic cases that are difficult to identify and probably were never reported. Due to these issues, even the CDC COVID Data Tracker and COVID-19 Dashboard at John Hopkins University do not provide data about race in all states, limiting what can be studied about geographic and longitudinal racial health disparities during this pandemic.
Past research and this study have revealed that Twitter data can be used to correlate with the increased public interest in certain emergencies, such as predicting public health outbreaks [, ] and using Twitter as a disaster situation awareness tool [ ]. Twitter data are a good indicator to assess the racial/ethnic bias of the pandemic. However, we also observed that COVID-19 and race/ethnicity-related tweets significantly decreased with extended time frame data due to the aforementioned reasons. Thus, the effect of Twitter data as a surveillance indicator to inform epidemiologic trends may attenuate with time. Several reasons may have contributed to this change. For instance, the lockdown after the COVID-19 outbreak in the United States naturally brought the nation’s attention to the pandemic, and an unusually high number of people turned to Twitter to obtain or share COVID-19–related news. The politicization of the pandemic combined with the spreading of misinformation and cyber-racism [ ] as well as the increase in vaccination probably contributed to a reduction in COVID-19–related tweets as we did not retrieve a significant number of tweets about COVID and race/ethnicity in December 2021 to replicate the study for the fifth wave. The continuation of this pandemic, combined with other extreme events and the economic situation that has impacted the country since 2020, probably has desensitized the public about the disproportionate impact of COVID-19 on certain racial/ethnical groups, thereby contributing to a reduction in tweets as well. Despite these limitations, similar to Cuomo et al [ ], we used the data from the first month of the pandemic to understand the racial/ethnic biases of the pandemic. Hence, public health–related studies could replicate our study to explore the racial and ethnic aspects of public health problems to aid health officials with scientific communication and appropriate response measures.
This research is an effort to test the possibility of using racially/ethnically stratified Twitter data as an info surveillance indicator to estimate the impact of COVID-19 on racial/ethnic groups in the United States to inform public health crisis response efforts and racial/ethnic equity. Using COVID-19 and geotagged racial/ethnic group–related Twitter data in the United States during April 2020, we filtered conversations about racial/ethnic groups in the early onset of the COVID-19 pandemic. Using the state-level counts of COVID-19 cases and deaths stratified by racial/ethnic groups, we found a strong correlation of tweet volume with COVID-19 deaths among Asians, Blacks, Latinos, and Whites and with COVID-19 cases among Asians. These findings demonstrate that racially/ethnically stratified Twitter data, as a surveillance indicator, could inform epidemiologic trends and help estimate the future surge of COVID-19 or other public health–related cases, deaths, and potential outbreaks of mutant viruses. The observed differential impacts on racial/ethnic minorities could guide public health policies to address racial/ethnic health disparities and deploy appropriate interventions (ie, more robust COVID-19 data collection about race and tailored measures to help racial/ethnic minorities).
The Division of Intramural Research, National Institute on Minority Health and Health Disparities, National Institutes of Health supported the efforts by XL, FAMI, and FW. The content is solely the authors' responsibility and does not necessarily reflect the views of the National Institutes of Health. This manuscript has been authored in part by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). TO has participated in this project independently and not on behalf of Huntsman Cancer Institute, the University of Utah, or its affiliates or successors. The views and conclusions expressed in this article are those of the authors. They do not reflect the policies or opinions of the funding agency, Oak Ridge National Laboratory, UT-Battelle, the Department of Energy, the US Government, or the University of Utah.
This research was supported by ZIA MD000015 Health Disparities & Geospatial transdisciplinary Research Lab.
Conflicts of Interest
Keywords used to stream Twitter data.DOCX File , 12 KB
- Van Goethem N, Vilain A, Wyndham-Thomas C, Deblonde J, Bossuyt N, Lernout T, et al. Rapid establishment of a national surveillance of COVID-19 hospitalizations in Belgium. Arch Public Health 2020 Nov 18;78(1):121 [FREE Full text] [CrossRef] [Medline]
- Estimated U.S. social media usage increase due to coronavirus home isolation 2020. Statistia. 2022 Apr 28. URL: https://www.statista.com/statistics/1106343/social-usage-increase-due-to-coronavirus-home-usa/ [accessed 2022-05-21]
- United States Coronavirus (COVID-19) Death Toll Surpasses 100,000. Center for Disease Control and Prevention. 2020 May 28. URL: https://www.cdc.gov/nchs/nvss/vsrr/covid19/index.htm [accessed 2022-05-21]
- 1Point3Acres. URL: https://coronavirus.1point3acres.com/ [accessed 2022-05-21]
- Rosenthal M. Fauci: COVID-19 Worst Pandemic in 100 Years. IDSE. 2020 Oct 21. URL: https://www.idse.net/Covid-19/Article/10-20/Fauci--COVID-19-Worst-Pandemic-in-100-Years/60937 [accessed 2022-05-21]
- Carratala S, Maxwell C. Health Disparities by Race and Ethnicity. Center for American Progress. 2020 May 07. URL: https://www.americanprogress.org/article/health-disparities-race-ethnicity/ [accessed 2022-05-21]
- Webb Hooper M, Nápoles AM, Pérez-Stable EJ. COVID-19 and racial/ethnic disparities. JAMA 2020 Jun 23;323(24):2466-2467. [CrossRef] [Medline]
- Chowkwanyun M, Reed AL. Racial health disparities and Covid-19 — caution and context. N Engl J Med 2020 Jul 16;383(3):201-203. [CrossRef]
- Roy A, Kar B. A multicriteria decision analysis framework to measure equitable healthcare access during COVID-19. J Transp Health 2022 Mar;24:101331 [FREE Full text] [CrossRef] [Medline]
- Kantamneni N. The impact of the COVID-19 pandemic on marginalized populations in the United States: A research agenda. J Vocat Behav 2020 Jun;119:103439 [FREE Full text] [CrossRef] [Medline]
- Flores LY, Martinez LD, McGillen GG, Milord J. Something old and something new: future directions in vocational research with people of color in the United States. Journal of Career Assessment 2019 Jan 07;27(2):187-208. [CrossRef]
- Broniatowski DA, Paul MJ, Dredze M. National and local influenza surveillance through Twitter: an analysis of the 2012-2013 influenza epidemic. PLoS One 2013;8(12):e83672 [FREE Full text] [CrossRef] [Medline]
- Lee K, Agrawal A, Choudhary A. Real-time disease surveillance using Twitter data: demonstration on flu and cancer. 2013 Presented at: 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; August 11-14, 2013; Chicago, IL. [CrossRef]
- Heaivilin N, Gerbert B, Page JE, Gibbs JL. Public health surveillance of dental pain via Twitter. J Dent Res 2011 Sep;90(9):1047-1051 [FREE Full text] [CrossRef] [Medline]
- Velardi P, Stilo G, Tozzi AE, Gesualdo F. Twitter mining for fine-grained syndromic surveillance. Artif Intell Med 2014 Jul;61(3):153-163. [CrossRef] [Medline]
- Dredze M. How social media will change public health. IEEE Intell. Syst 2012 Jul;27(4):81-84 [FREE Full text] [CrossRef]
- Conway M. Ethical issues in using Twitter for public health surveillance and research: developing a taxonomy of ethical concepts from the research literature. J Med Internet Res 2014 Dec 22;16(12):e290 [FREE Full text] [CrossRef] [Medline]
- Hu Y, Wang R. Understanding the removal of precise geotagging in tweets. Nat Hum Behav 2020 Dec 07;4(12):1219-1221. [CrossRef] [Medline]
- Nguyen QC, McCullough M, Meng H, Paul D, Li D, Kath S, et al. Geotagged US tweets as predictors of county-level health outcomes, 2015–2016. Am J Public Health 2017 Nov;107(11):1776-1782. [CrossRef]
- Liu X, Kar B, Zhang C, Cochran DM. Assessing relevance of tweets for risk communication. International Journal of Digital Earth 2018 Jun;12(7):781-801. [CrossRef]
- National Health Interview Survey (NHIS) Restricted Variables. Centers for Disease Control and Prevention. URL: https://www.cdc.gov/rdc/b1datatype/Dt1225.htm [accessed 2022-05-21]
- Encrypted or Restricted Variables. National Cancer Insitute. URL: https://healthcaredelivery.cancer.gov/seermedicare/privacy/variables.html [accessed 2022-05-21]
- Nguyen TT, Criss S, Dwivedi P, Huang D, Keralis J, Hsu E, et al. Exploring U.S. shifts in anti-Asian sentiment with the emergence of COVID-19. Int J Environ Res Public Health 2020 Sep 25;17(19):7032 [FREE Full text] [CrossRef] [Medline]
- Rufai S, Bunce C. World leaders' usage of Twitter in response to the COVID-19 pandemic: a content analysis. J Public Health (Oxf) 2020 Aug 18;42(3):510-516 [FREE Full text] [CrossRef] [Medline]
- Guntuku SC, Sherman G, Stokes DC, Agarwal AK, Seltzer E, Merchant RM, et al. Tracking mental health and symptom mentions on Twitter during COVID-19. J Gen Intern Med 2020 Sep 07;35(9):2798-2800 [FREE Full text] [CrossRef] [Medline]
- Al-Ramahi M, Elnoshokaty A, El-Gayar O, Nasralah T, Wahbeh A. Public discourse against masks in the COVID-19 era: infodemiology study of Twitter data. JMIR Public Health Surveill 2021 Apr 05;7(4):e26780 [FREE Full text] [CrossRef] [Medline]
- Pobiruchin M, Zowalla R, Wiesner M. Temporal and location variations, and link categories for the dissemination of COVID-19-related information on Twitter during the SARS-CoV-2 outbreak in Europe: infoveillance study. J Med Internet Res 2020 Aug 28;22(8):e19629 [FREE Full text] [CrossRef] [Medline]
- Cheng IK, Heyl J, Lad N, Facini G, Grout Z. Evaluation of Twitter data for an emerging crisis: an application to the first wave of COVID-19 in the UK. Sci Rep 2021 Sep 24;11(1):19009 [FREE Full text] [CrossRef] [Medline]
- Turiel J, Fernandez-Reyes D, Aste T. Wisdom of crowds detects COVID-19 severity ahead of officially available data. Sci Rep 2021 Jul 01;11(1):13678 [FREE Full text] [CrossRef] [Medline]
- Roesslein J. tweepy/tweepy. GitHub. URL: https://github.com/tweepy/tweepy [accessed 2022-05-21]
- Racial Data Dashboard. The COVID Tracking Project. URL: https://covidtracking.com/race/dashboard [accessed 2022-05-21]
- 2015-2019 ACS 5-year Estimates. United States Census Bureau. URL: https://www.census.gov/programs-surveys/acs/technical-documentation/table-and-geography-changes/2019/5-year.html [accessed 2022-05-21]
- Cuomo RE, Purushothaman V, Li J, Cai M, Mackey TK. Sub-national longitudinal and geospatial analysis of COVID-19 tweets. PLoS One 2020 Oct 28;15(10):e0241330 [FREE Full text] [CrossRef] [Medline]
- COVID-19 Forecasts: Cases. Centers for Disease Control and Prevention. URL: https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/forecasts-cases.html [accessed 2022-05-21]
- Hardy LJ, Mana A, Mundell L, Neuman M, Benheim S, Otenyo E. Who is to blame for COVID-19? Examining politicized fear and health behavior through a mixed methods study in the United States. PLoS One 2021 Sep 1;16(9):e0256136 [FREE Full text] [CrossRef] [Medline]
- Alwan NA. Surveillance is underestimating the burden of the COVID-19 pandemic. The Lancet 2020 Sep;396(10252):e24. [CrossRef]
- McCormick TH, Lee H, Cesare N, Shojaie A, Spiro ES. Using Twitter for demographic and social science research: tools for data collection and processing. Sociol Methods Res 2017 Aug 09;46(3):390-421 [FREE Full text] [CrossRef] [Medline]
- Twitter: distribution of global audiences 2021, by age group. Statista. 2022 Mar 29. URL: https://www.statista.com/statistics/283119/age-distribution-of-global-twitter-users/ [accessed 2022-05-21]
- Shankar K, Jeng W, Thomer A, Weber N, Yoon A. Data curation as collective action during COVID‐19. J Assoc Inf Sci Technol 2020 Sep 02;72(3):280-284. [CrossRef]
- Carr N. Public health experts call out confusing COVID-19 data maps; DPH set to make changes. WSB-TV Channel 2 - Atlanta. URL: https://www.wsbtv.com/news/georgia/public-health-experts-call-out-confusing-covid-19-data-maps-dph-set-make-changes/KZPBOLBG2BG2TAMI2N4CQGWESA/ [accessed 2022-05-21]
- Wamsley L. Fired Florida Data Scientist Launches A Coronavirus Dashboard Of Her Own. NPR. 2020 Jun 14. URL: https://www.npr.org/2020/06/14/876584284/fired-florida-data-scientist-launches-a-coronavirus-dashboard-of-her-own [accessed 2022-05-21]
- Karami A, Shah V, Vaezi R, Bansal A. Twitter speaks: A case of national disaster situational awareness. Journal of Information Science 2019 Mar 04;46(3):313-324. [CrossRef]
- Dubey AD. The resurgence of cyber racism during the COVID-19 pandemic and its aftereffects: analysis of sentiments and emotions in tweets. JMIR Public Health Surveill 2020 Oct 15;6(4):e19833 [FREE Full text] [CrossRef] [Medline]
|API: application programming interface|
|CDC: Centers for Disease Control and Prevention|
|DOE: Department of Energy|
|OLS: ordinary least squares|
Edited by A Mavragani; submitted 11.05.21; peer-reviewed by M Alvarez de Mon, M Nomali, S Doan, T Vukušić Rukavina; comments to author 23.08.21; revised version received 29.12.21; accepted 14.04.22; published 30.05.22Copyright
©Xiaohui Liu, Bandana Kar, Francisco Alejandro Montiel Ishino, Tracy Onega, Faustine Williams. Originally published in JMIR Formative Research (https://formative.jmir.org), 30.05.2022.
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