Temporal Relationship between Daily Reports of COVID-19 Infections and Related GDELT and Tweet Mentions
Abstract
:1. Introduction
- Assess time-lagged relationships between new COVID-19 cases and the number of COVID-19-related GDELT articles and tweets in selected countries using cross-correlation analysis.
- Identify anomalies and their causes on days with abnormally high COVID-19-related responses on GDELT and Twitter but low numbers of new COVID-19 cases.
2. Literature Review
3. Materials and Methods
3.1. Data Sources
3.1.1. New Daily COVID-19 Infections
3.1.2. Twitter
3.1.3. GDELT
3.2. Data Preprocessing
3.3. Cross-Correlation Analysis
3.3.1. Step 1: Time Series Decomposition
3.3.2. Step 2: Time Series Transformation and Differencing
3.3.3. Step 3: Fitting an ARIMA Model to the Input Series
3.3.4. Steps 4 and 5: Prewhitening and Cross-Correlation of Residuals
3.3.5. Steps 6 and 7: Vector Autoregressive Models and Cross-Correlation of Residuals
3.4. Anomaly Detection
3.5. Word Frequency Analysis
4. Results
4.1. Cross-Correlation
4.1.1. Positive Lag
4.1.2. Negative Lag
4.1.3. Positive and Negative Lag
4.2. Anomaly Detection
5. Discussion
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A
Lag | VAR (COVID-19 vs. GDELT) | Lag | VAR (COVID-19 vs. Twitter) | |||
---|---|---|---|---|---|---|
COVID-19 | GDELT | COVID-19 | ||||
U.K. | GDELT (1) | −0.429 *** (0.128) | Twitter (2) | −0.546 *** (0.125) | ||
GDELT (2) | −0.291 ** (0.141) | Twitter (6) | 0.218 * (0.126) | |||
COVID-19 (5) | −0.087 * (0.045) | COVID-19 (7) | 0.229 ** (0.123) | |||
COVID-19 (7) | 0.217 * (0.122) | Twitter (4) | 0.213 * (0.123) | |||
N | 82 | 82 | 82 | 82 | ||
R2 | 0.482 | 0.281 | 0.448 | 0.416 | ||
Adjusted R2 | 0.312 | 0.046 | 0.267 | 0.225 | ||
Philippines | COVID-19 (1) | −0.615 *** (0.101) | 5.450 ** (2.173) | COVID-19 (1) | −0.599 *** (0.121) | |
GDELT (1) | −0.448 *** (0.103) | Twitter (1) | 0.191 * (0.114) | −0.594 *** (0.118) | ||
COVID-19 (2) | −0.475 *** (0.105) | COVID-19 (2) | −0.449 ** (0.138) | |||
GDELT (2) | −0.421 *** (0.098) | Twitter (2) | −0.375 * (0.132) | |||
Twitter (3) | −0.301 ** (0.131) | |||||
N | 87 | 87 | 82 | 82 | ||
R2 | 0.391 | 0.364 | 0.449 | 0.352 | ||
Adjusted R2 | 0.311 | 0.281 | 0.338 | 0.222 | ||
Germany | COVID-19 (1) | −0.625 *** (0.113) | 0.011 ** (0.005) | COVID-19 (1) | −0.653 *** (0.125) | |
GDELT (1) | −0.665 *** (0.121) | Twitter (3) | 1.692 * (0.915) | |||
GDELT (2) | −0.513 *** (0.145) | COVID-19 (4) | 0.322 ** (0.132) | |||
GDELT (3) | −0.430 *** (0.150) | Twitter (4) | 0.213 * (0.123) | |||
COVID-19 (4) | 0.302 ** (0.131) | COVID-19 (5) | 0.443 *** (0.136) | |||
COVID-19 (5) | 0.386 *** (0.112) | Twitter (6) | 2.079 ** (0.937) | |||
N | 84 | 84 | 82 | 82 | ||
R2 | 0.522 | 0.418 | 0.598 | 0.322 | ||
Adjusted R2 | 0.408 | 0.280 | 0.467 | 0.099 |
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Lag | VAR (COVID-19 and GDELT) | Lag | VAR (COVID-19 and Twitter) | ||
---|---|---|---|---|---|
COVID-19 | GDELT | COVID-19 | |||
COVID-19 (1) | −0.860 *** (0.122) | COVID-19 (1) | −0.865 *** (0.22) | ||
GDELT (1) (1) | −0.289 ** (0.121) | Twitter (1) | 0.002 * (0.001) | ||
GDELT (2) | −0.288 *** (0.125) | Twitter (2) | −0.466 *** (0.120) | ||
GDELT (3) | 0.029 * (0.015) | Twitter (3) | 0.303 * (0.130) | ||
COVID-19 (5) | 0.346 ** (0.156) | Twitter (4) | 0.002 * (0.001) | ||
COVID-19 (6) | 0.234 * (0.122) | Twitter (5) | 2.079 ** (0.937) | 0.334 *** (0.124) | |
N | 83 | 83 | 84 | 84 | |
R2 | 0.614 | 0.219 | 0.601 | 0.385 | |
Adjusted R2 | 0.506 | 0.091 | 0.506 | 0.238 |
Country | Model | Time Lag in Days | Quadrant | |
---|---|---|---|---|
COVID-19 vs. GDELT | COVID-19 vs. Twitter | |||
Australia | ARIMA (1, 0, 2) | 12 | 3 | 1 |
Brazil | ARIMA (0, 0, 1) with nonzero mean | 7 | 10 | 1 |
France | ARIMA (0, 0, 1) | none | 8 | 1 |
Greece | ARIMA (0, 0, 1) | 1 | 0 | 1 |
India | ARIMA (4, 0, 0) with nonzero mean | 7 | 14 | 1 |
Italy | ARIMA (0, 1, 2) (0, 0, 1)7 | 16 | 1 | 1 |
Poland | ARIMA (2, 0, 0) with nonzero mean | −16 | 0 | 1 and 3 |
U.S. | ARIMA (0, 1, 2) (1, 0, 1)7 | 7 | 5 | 1 |
Canada | VAR (6)—COVID-19 and GDELT VAR (5)—COVID-19 and Twitter | 0 | 11 | 1 |
Germany | VAR (5)—COVID-19 and GDELT VAR (7)—COVID-19 and Twitter | 7 | 11 | 1 |
Philippines | VAR (2)—COVID-19 and GDELT VAR (4)—COVID-19 and Twitter | −7 | 4 | 1 and 3 |
U.K. | VAR (7)—COVID-19 and GDELT VAR (7)—COVID-19 and Twitter | 15 | 13 | 1 |
Country | Anomaly Date | Frequent Words | Events |
---|---|---|---|
Bangladesh | 2020-05-04 | holidays | National holiday |
Bolivia | 2020-10-19 | election, party, victory | General elections |
Botswana | 2020-7-31 | requirements, compliant | Introduction of lockdown |
Cyprus | 2021-03-07 | Cyprus, protest | Protests |
Guatemala | 2020-09-19 | president | President of Guatemala contracted COVID-19 |
Jamaica | 2020-08-25 | Usain Bolt | Jamaican Olympian contracted COVID-19 |
Lebanon | 2020-08-05 | explosion, deadly, Beirut | Beirut port explosion |
Netherlands | 2021-03-03 | explosion | Explosion |
Serbia | 2020-07-08 | protest, violent | Protests |
Singapore | 2020-12-09 | cruise | COVID-19 scare on a cruise ship |
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Owuor, I.; Hochmair, H.H. Temporal Relationship between Daily Reports of COVID-19 Infections and Related GDELT and Tweet Mentions. Geographies 2023, 3, 584-609. https://doi.org/10.3390/geographies3030031
Owuor I, Hochmair HH. Temporal Relationship between Daily Reports of COVID-19 Infections and Related GDELT and Tweet Mentions. Geographies. 2023; 3(3):584-609. https://doi.org/10.3390/geographies3030031
Chicago/Turabian StyleOwuor, Innocensia, and Hartwig H. Hochmair. 2023. "Temporal Relationship between Daily Reports of COVID-19 Infections and Related GDELT and Tweet Mentions" Geographies 3, no. 3: 584-609. https://doi.org/10.3390/geographies3030031