Using geospatial social media data for infectious disease studies: a systematic review

Figures & data

Figure 1. Flow diagram for article identification and selection.

Figure 2. Number of publications on the use of GSM data on infectious diseases (As of March 1, 2022).

Figure 3. Summary of the characteristics of the included papers.

Figure 4. Number of publications by infectious diseases.

Table 1. Related articles involving the surveillance.

Information type	Research topics	Social media data	Main methods	Main geographic level
Contextual information	COVID-19 transmission (Cuomo et al. 2020; Peng et al. 2020; Pobiruchin, Zowalla, and Wiesner 2020; Cuomo et al. 2021; Lopreite et al. 2021)	COVID-19- related Tweets (Cuomo et al. 2020; Pobiruchin, Zowalla, and Wiesner 2020; Cuomo et al. 2021) and Weibo (Peng et al. 2020)	Spatiotemporal analysis (Cuomo et al. 2020; Peng et al. 2020; Pobiruchin, Zowalla, and Wiesner 2020; Cuomo et al. 2021) Geospatial cluster analysis (Cuomo et al. 2020, 2021) Kernel Density Analysis (Peng et al. 2020)	Point level (Peng et al. 2020; Pobiruchin, Zowalla, and Wiesner 2020) Census tract level (Cuomo et al. 2021)
	Attitudes and perceptions of the public during COVID-19 (Han et al. 2020; Hung et al. 2020; Yigitcanlar et al. 2020) Public opinions on COVID-19 vaccination (Ali et al. 2021; Guntuku et al. 2021; Hu et al. 2021; Liu and Liu 2021) Reaction to COVID-19 lockdown (Li, Erfani, et al. 2021; Surano, Porfiri, and Rizzo 2022) Public perception of COVID-19 contact tracing apps (Cresswell et al. 2021) Political Partisanship and Antiscience Attitudes in COVID-19 Internet Discussions (Rao et al. 2021)	Tweets related to COVID-19 (Hung et al. 2020; Yigitcanlar et al. 2020; Rao et al. 2021) Weibo texts related to COVID-19 (Han et al. 2020). Tweets related to COVID-19 vaccination (Ali et al. 2021; Guntuku et al. 2021; Hu et al. 2021; Liu and Liu 2021; Yousefinaghani et al. 2021) Tweets related to COVID-19 lockdown (Li, Erfani, et al. 2021; Surano, Porfiri, and Rizzo 2022) Facebook and Twitter posts related to public perceptions of COVID-19 contacting tracking apps (Cresswell et al. 2021)	Spatiotemporal analysis (Han et al. 2020; Yigitcanlar et al. 2020; Cresswell et al. 2021; Hu et al. 2021; Liu and Liu 2021; Rao et al. 2021) Social network analyses (Hung et al. 2020) Content analysis (Han et al. 2020; Yigitcanlar et al. 2020; Li, Erfani, et al. 2021; Yousefinaghani et al. 2021) Sentiment analysis (Hung et al. 2020; Yigitcanlar et al. 2020; Ali et al. 2021; Cresswell et al. 2021; Hu et al. 2021; Liu and Liu 2021; Surano, Porfiri, and Rizzo 2022) Emotion analysis (Hu et al. 2021) Word cloud mapping (Guntuku et al. 2021; Hu et al. 2021; Yousefinaghani et al. 2021) and topic modeling (Guntuku et al. 2021; Hu et al. 2021; Rao et al. 2021) Pay-level domains (PLDs) (Rao et al. 2021)	Provincial level (Han et al. 2020) State level (Hung et al. 2020; Yigitcanlar et al. 2020; Hu et al. 2021; Li, Erfani, et al. 2021; Liu and Liu 2021; Surano, Porfiri, and Rizzo 2022) County level (Ali et al. 2021; Cresswell et al. 2021; Guntuku et al. 2021; Rao et al. 2021; Yousefinaghani et al. 2021)
	Features of online topics and emotional changes during COVID-19 lockdown (Zhu et al. 2020) Facilities use under COVID-19 restrictions (Legeby et al. 2022) Practices by health account during COVID-19 (Slavik et al. 2021) Rumors during the Pandemic (Chen et al. 2021) Assess regional dependence of online sentiments on COVID-19 topics (Feng and Kirkley 2021)	Weibo texts (Zhu et al. 2020). Twitter activity (Legeby et al. 2022) COVID-19-related tweets by public health agencies and policymakers (Slavik et al. 2021) Online rumors related to COVID-19 on Sina Weibo (Chen et al. 2021) COVID-related tweets (Feng and Kirkley 2021)	Topic modeling (LDA) (Zhu et al. 2020) Sentiment analysis (Zhu et al. 2020; Feng and Kirkley 2021) Two-sample Kolmogrov-Smirnov test (Legeby et al. 2022) Social network analyses. Kruskal-Wallis one-way ANOVA test, and nonparametric Mann–Whitney–Wilcoxon Test (Slavik et al. 2021) Spatiotemporal analysis, Pearson chi-square test, Fisher exact test, and Word cloud mapping (Feng and Kirkley 2021) Time-series analyses (Feng and Kirkley 2021)	Provincial level (Zhu et al. 2020; Chen et al. 2021; Slavik et al. 2021) Neighborhood level (Legeby et al. 2022) Metropolitan Statistical Areas (Feng and Kirkley 2021)
	Characterization of HIV outbreak (Cai et al. 2020; van Heerden and Young 2020)	Tweets related to the 2015 Indiana HIV outbreak (Cai et al. 2020) HIV-related social media posts (Twitter, Instagram, and YouTube) (van Heerden and Young 2020)	Biterm Topic Model (BTM) (Cai et al. 2020) Spatial analysis (Cai et al. 2020; van Heerden and Young 2020)	Point level (Cai et al. 2020; van Heerden and Young 2020)
	Features of influenza (Yang, Lipsitch, and Shaman 2015)	Google Flu Trends	SIRS Model	City level
	Citizen reactions to Ebola-related information (Liu et al. 2016; Tran and Lee 2016)	Ebola Tweets (Tran and Lee 2016) and Baidu Index and Sina Micro Index (Liu et al. 2016)	Spatiotemporal analysis (Tran and Lee 2016)	Country level (Tran and Lee 2016) Provincial level (Liu et al. 2016)
	Public perceptions of Zika outbreak (Pruss et al. 2019)	Tweets related to Zika	Topic modeling and spatiotemporal analysis	Country level
	Public perceptions and risk communication in MERS outbreak (Kim et al. 2017)	Tweets related to MERS	Spatiotemporal analysis	City level
	Dengue patterns (Nsoesie et al. 2016; Ye et al. 2016)	Weibo text (Ye et al. 2016) and Tweets (Nsoesie et al. 2016) related to Dengue	Keywords, topic modeling, and Kalman filter (Ye et al. 2016) Spatiotemporal analysis (Nsoesie et al. 2016; Ye et al. 2016)	Provincial level (Ye et al. 2016) State level (Nsoesie et al. 2016)
Movement information	Spread of COVID-19 (Chang et al. 2021; Cowley et al. 2021)	Facebook mobility data (Chang et al. 2021; Cowley et al. 2021)	Spatiotemporal analysis (Chang et al. 2021; Cowley et al. 2021)	National level (Cowley et al. 2021) County level (Chang et al. 2021)
	Changes in human movement patterns during COVID-19 (Huang et al. 2020; Xu, Dredze, and Broniatowski 2020; Chang et al. 2021; Gibbs et al. 2021; Gulnerman 2021; Iranmanesh and Alpar Atun 2021; Shepherd et al. 2021)	Mobility data aggregated from Facebook (Chang et al. 2021; Gibbs et al. 2021; Shepherd et al. 2021) and Twitter (Huang et al. 2020; Xu, Dredze, and Broniatowski 2020; Gulnerman 2021) Mean distances between geotagged tweets (Iranmanesh and Alpar Atun 2021)	Community detection (Gibbs et al. 2021) Spatiotemporal analysis (Chang et al. 2021; Gulnerman 2021; Iranmanesh and Alpar Atun 2021; Shepherd et al. 2021) Kernel Density Estimation (Iranmanesh and Alpar Atun 2021) Single-day/cross-day distance, normalized mobility indicator, and Mobility-based responsive indicator (Huang et al. 2020)	Territorial units for statistics level (Shepherd et al. 2021) Lower Layer Super Output areas (Gibbs et al. 2021) Neighborhood level (Gulnerman 2021) City level (Xu, Dredze, and Broniatowski 2020) and Whole city (Iranmanesh and Alpar Atun 2021) State level (Huang et al. 2020) County level (Chang et al. 2021)
	Temporal variation of dengue spread (Kraemer et al. 2018)	Twitter-based human mobility	Transmission model based on TSIR framework	Town level
Network information	Discordant responses to COVID-19 (Holtz et al. 2020)	Social network connections on Facebook.	Difference-in-differences (DiD) model	County level

Table 2. Related articles involving the explanation.

Information type	Research topics	Social media data	Main methods	Main geographic level
Contextual information	Tweets and actual deaths (Dahal et al. 2021; Turiel, Fernandez-Reyes, and Aste 2021) The link between tweets cases and officially COVID cases (Shen et al. 2020; Cuomo et al. 2021) The relationship between twitter vaccine content and COVID vaccination and attitude (Chan, Jamieson, and Albarracin 2020)	Tweet intensity (Turiel, Fernandez-Reyes, and Aste 2021) COVID-19-death-related tweets (Dahal et al. 2021) Tweets related to COVID-19 (Cuomo et al. 2021) Weibo posts related to COVID-19 (Shen et al. 2020) Twitter vaccine content (Chan, Jamieson, and Albarracin 2020)	The Weighted Least Squares (WLS) function and Ordinary Least Squares (OLS) function (Turiel, Fernandez-Reyes, and Aste 2021) correlation coefficients (Dahal et al. 2021) Bivariate regressions (Cuomo et al. 2021) Granger causality tests (Shen et al. 2020) Intra-class correlation and Bayesian correlation (Chan, Jamieson, and Albarracin 2020)	Regional level (Dahal et al. 2021; Turiel, Fernandez-Reyes, and Aste 2021) County level (Chan, Jamieson, and Albarracin 2020; Cuomo et al. 2021) Whole country (Shen et al. 2020)
	COVID concerns and socioeconomic disparities (Su et al. 2021) The impact of socioeconomic variables on online feelings about COVID-19 (Scotti et al. 2020) Association between social media activity and COVID-19 cases (Forati and Ghose 2021) Association between social distancing beliefs and human mobility (Porcher and Renault 2021)	Twitter related to COVID-19 (Scotti et al. 2020; Forati and Ghose 2021; Su et al. 2021) Twitter related to social distancing beliefs (Porcher and Renault 2021)	Chi-squared test, independent t-test, and spatiotemporal analysis (Su et al. 2021) Panel regression analysis; cross-section regressions (Scotti et al. 2020) Geographically Weighted Regression (GWR) (Forati and Ghose 2021) OLS model (Porcher and Renault 2021)	County level (Forati and Ghose 2021; Su et al. 2021) Provincial level (Scotti et al. 2020) State level (Porcher and Renault 2021)
	Neighborhood environment and COVID-19 cases (Li et al. 2020; Peng et al. 2020) Lockdown and socioeconomic factors (Surano, Porfiri, and Rizzo 2022) Social media activity and COVID-19 cases (Forati and Ghose 2021) The impact of COVID-19 on mental health (Zhu et al. 2021)	Weibo related to COVID-19 cases (Li et al. 2020; Peng et al. 2020) Tweets related to COVID-19 lockdown (Surano, Porfiri, and Rizzo 2022) Tweets related to COVID-19 (Forati and Ghose 2021) Weibo texts (Zhu et al. 2021) (Zhu et al. 2021)	Geographically weighted regression (GWR) (Li et al. 2020; Forati and Ghose 2021) Ordinary Least Squares (OLS) Regression (Peng et al. 2020) Kendall-τ coefficients (Surano, Porfiri, and Rizzo 2022) DiD model (Zhu et al. 2021)	Neighborhood level (Li et al. 2020) Traffic analysis zone (Peng et al. 2020) City level (Zhu et al. 2021) State level (Forati and Ghose 2021; Surano, Porfiri, and Rizzo 2022) County level (Forati and Ghose 2021)
	Tweets related to HIV cases and actual HIV cases (Young, Rivers, and Lewis 2014; Nielsen et al. 2017; Li, Qiao, et al. 2021)	Tweets related to HIV (Young, Rivers, and Lewis 2014; Nielsen et al. 2017; Stevens et al. 2020; Li, Qiao, et al. 2021)	Multivariate regression (Li, Qiao, et al. 2021) Correlation analysis (Nielsen et al. 2017) Negative binomial regressions (Young, Rivers, and Lewis 2014; Stevens et al. 2020) Wilcoxon rank-sum test with continuity correction (Stevens et al. 2020)	County level (Young, Rivers, and Lewis 2014; Stevens et al. 2020; Li, Qiao, et al. 2021) City level (Nielsen et al. 2017)
	Association of HIV tweets and pre-exposure prophylaxis (Chan et al. 2021)	HIV related tweets	Intra-class correlations (ICCs) and Bayesian statistical analyses	County level
	Action language and HIV prevalence (Ireland et al. 2016)	Action language tweets	Linear mixed models	County level
	Dengue tweets and actual Dengue cases (Nsoesie et al. 2016; Puspitasari, Ariful, and Nuqoba 2021)	Dengue Instagram posts (Puspitasari, Ariful, and Nuqoba 2021) and tweets (Nsoesie et al. 2016)	Spatial analysis (Puspitasari, Ariful, and Nuqoba 2021) Univariate linear regression models (Nsoesie et al. 2016)	Provincial level (Puspitasari, Ariful, and Nuqoba 2021) Municipality level (Nsoesie et al. 2016)
	Factors influencing dengue (Nsoesie et al. 2016)	Twitter related to Dengue fever	Logistic multivariable model	Municipality level
	Factors influencing Syphilis (Young et al. 2018)	Tweets with sexual and syphilis	Negative binomial regression	County level
	Influenza-like tweets and actual cases (Broniatowski, Paul, and Dredze 2013; Nagar et al. 2014; Allen et al. 2016; Wang et al. 2016; Chen et al. 2019) Vaccine tweets and actual influenza vaccination activity (Huang et al. 2019) Gender differences on influenza vaccine (Huang et al. 2019)	Influenza Tweets (Broniatowski, Paul, and Dredze 2013; Allen et al. 2016) Influenza vaccine Tweets (Nagar et al. 2014; Wang et al. 2016; Huang et al. 2019) Google trends (Nagar et al. 2014) Baidu Search Index (BSI) and Weibo Post Index (WPI) search data for H7N9 (Chen et al. 2019)	Correlation analysis (Allen et al. 2016; Wang et al. 2016; Huang et al. 2019) Cross-correlation functions and linear regression (Broniatowski, Paul, and Dredze 2013) Linear regressions and Autoregressive integrated moving Average (ARIMA) model (Huang et al. 2019) Linear regressions (Nagar et al. 2014) Seasonal autoregressive integrated moving average (SARIMA) (Chen et al. 2019)	City level (Nagar et al. 2014; Allen et al. 2016) Whole city and country (Broniatowski, Paul, and Dredze 2013) State level (Huang et al. 2019) Regional level (Wang et al. 2016) Provincial level (Chen et al. 2019)
	Eating chicken feet and avian influenza (Chen et al. 2018)	Sina Weibo and Tencent Weibo texts related to influenza	Content analyses, Pearson chi-square test, and Kruskal Wallis tests	Provincial level
	Groups characteristics in response to Zika. (Daughton and Paul 2019)	Tweets related to Zika virus	A weighted bootstrapped CI	Regional level
Movement information	Population mobility and COVID-19 (Zeng et al. 2021) Labor market and COVID-19 spread (Ascani et al. 2021)	Twitter-based population mobility (Zeng et al. 2021) Facebook high-frequency mobility data (Ascani et al. 2021)	Poisson count time series model (Zeng et al. 2021) Fixed-effect model (Ascani et al. 2021)	County level (Zeng et al. 2021) Local market areas (Ascani et al. 2021)
Network information	Mobility and social connectivity and COVID-19 infections (Fritz and Kauermann 2022)	Human mobility and social connectivity derived from Facebook activities	Negative Binomial model	Federal administrative district (NUTS-3 level)
	Place connectivity/human mobility and COVID-19 infections (Fan et al. 2021; Li, Huang, Ye, et al. 2021)	Twitter place connectivity and Facebook social connectedness index (Li, Huang, Ye, et al. 2021) Facebook co-location data (Fan et al. 2021)	Linear regression models (Fan et al. 2021; Li, Huang, Ye, et al. 2021) Co-location weighted spatial network and Time-lagged cross-correlation (Fan et al. 2021)	County level (Fan et al. 2021; Li, Huang, Ye, et al. 2021)
	Impacts of county-level factors on COVID-19 trajectories (Li, Yang, et al. 2021)	Facebook-based social connectedness index	Random forest models	County level

Table 3. Related articles involving the prediction.

Information type	Research topics	Social media data	Main methods	Main geographic level
Contextual information	Prediction of COVID-19 cases (Fakhry, Asfoura, and Kassam 2020)	Tweets related to COVID-19	Naïve Bayes and SVM	State level
	Prediction of COVID-19-related health system overloads (Dahal et al. 2021; Rivieccio et al. 2021)	COVID-19-death-related tweets (Dahal et al. 2021; Rivieccio et al. 2021)	Time lag estimation through cross-correlation analysis and Wavelet analysis (Rivieccio et al. 2021) generalized logistic growth model (Dahal et al. 2021)	Regional level (Dahal et al. 2021; Rivieccio et al. 2021)
	Prediction of influenza (Nagar et al. 2014; Wang et al. 2016; Elkin, Topal, and Bebek 2017; Kandula, Hsu, and Shaman 2017; Lu et al. 2018; Wakamiya, Kawai, and Aramaki 2018; Chen et al. 2019; Wang et al. 2020)	Twitter related to influenza (Nagar et al. 2014; Wang et al. 2016; Elkin, Topal, and Bebek 2017; Lu et al. 2018; Wakamiya, Kawai, and Aramaki 2018; Wang et al. 2020) Google searches trends (Nagar et al. 2014; Kandula, Hsu, and Shaman 2017; Lu et al. 2018) Baidu Search Index (BSI) (Chen et al. 2019) Weibo Posting Index (WPI) (Chen et al. 2019)	Specific partial differential equation (PDE) model (Wang et al. 2016, 2020) Multivariate dynamic regression method named Autoregression with general online information (ARGO) (Lu et al. 2018) Supervised regression methods (i.e. Random forests) and Autoregressive integrated moving average (ARIMA) (Kandula, Hsu, and Shaman 2017) A novel network-based model (Elkin, Topal, and Bebek 2017) Linear Regression (Nagar et al. 2014) Seasonal Autoregressive integrated moving average (SARIMA) model and Regression tree model (Chen et al. 2019) TRAP model (Wakamiya, Kawai, and Aramaki 2018)	State level (Elkin, Topal, and Bebek 2017; Kandula, Hsu, and Shaman 2017; Wang et al. 2020) Provincial level (Chen et al. 2019) Health area level (Nagar et al. 2014) Whole city (Lu et al. 2018) Regional level (Wang et al. 2016) Whole country (Wakamiya, Kawai, and Aramaki 2018)
	HIV, Chlamydia, and Gonorrhea predictions (Chan et al. 2018)	Tweets related to HIV and sexually transmitted infections (STIs)	An extremely-randomized-trees model and two semantic models	County level
	Prediction of Campylobacter outbreaks (Adnan et al. 2020)	Twitter microblogs, and Google Trends	Multivariable autoregressive integrated moving average (ARIMA) models	Neighborhood level
Movement information	Dengue outbreaks (Ramadona et al. 2019) Prediction of COVID-19 cases (Bisanzio et al. 2020; Cowley et al. 2021; Zeng et al. 2021)	Twitter-based mobility (Ramadona et al. 2019; Bisanzio et al. 2020; Zeng et al. 2021) Facebook Data for Good (Cowley et al. 2021)	Bayesian spatiotemporal regression model (Ramadona et al. 2019) A Poisson count time series model (Zeng et al. 2021) Spatiotemporal analysis (Bisanzio et al. 2020) Bayesian time-scaled phylogenetic analysis (Cowley et al. 2021)	Neighborhood level (Ramadona et al. 2019) Whole state (Zeng et al. 2021) County level (Bisanzio et al. 2020) City level (Cowley et al. 2021)
Network information	Dengue outbreaks (Castro et al. 2021)	Twitter-based human connectivity	Wavelet analysis, Pearson's correlation, spatial autocorrelation, clustering analyses, and LASSO regression	Brazilian mesoregion level
Movement information and Network information	Prediction of COVID-19 cases (Vahedi, Karimzadeh, and Zoraghein 2021; Lucas, Vahedi, and Karimzadeh 2022)	Facebook-based connectivity and human mobility (Vahedi, Karimzadeh, and Zoraghein 2021; Lucas, Vahedi, and Karimzadeh 2022)	Spatiotemporal autoregressive model (Vahedi, Karimzadeh, and Zoraghein 2021) COVID-19 Public Forecast model (Google_Harvard-CPF), the Umass-MechBayes model, and Facebook AI Research's Neural Relational Autoregressive Model (FAIR-NRAR) (Lucas, Vahedi, and Karimzadeh 2022)	County level (Vahedi, Karimzadeh, and Zoraghein 2021) Country level (Lucas, Vahedi, and Karimzadeh 2022)
	Potential impact of COVID-19 travel restrictions (Chang et al. 2021)	Facebook-based mobility and colocation data	Susceptible-latent-infectious-recovered (SLIR) models.	County level

Cuomo, Raphael E., Vidya Purushothaman, Jiawei Li, Mingxiang Cai, and Timothy K. Mackey. 2020. “Sub-national Longitudinal and Geospatial Analysis of COVID-19 Tweets.” PLoS One 15 (10): e0241330. doi:10.1371/journal.pone.0241330.

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Peng, Zhenghong, Ru Wang, Lingbo Liu, and Hao Wu. 2020. “Exploring Urban Spatial Features of COVID-19 Transmission in Wuhan Based on Social Media Data.” ISPRS International Journal of Geo-Information 9 (6): 402. doi:10.3390/ijgi9060402

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Pobiruchin, Monika, Richard Zowalla, and Martin Wiesner. 2020. “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.” Journal of Medical Internet Research 22 (8): e19629. doi:10.2196/19629

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Hu, Tao, Siqin Wang, Wei Luo, Mengxi Zhang, Xiao Huang, Yingwei Yan, Regina Liu, Kelly Ly, Viraj Kacker, and Bing She. 2021. “Revealing Public Opinion Towards COVID-19 Vaccines with Twitter Data in the United States: Spatiotemporal Perspective.” Journal of Medical Internet Research 23 (09): e30854. doi:10.2196/30854.

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