Causal Modeling of Twitter Activity during COVID-19
Abstract
:1. Introduction
2. Going Beyond Correlations
3. Methods
3.1. Data
3.2. Feature Selection
3.3. Structure Learning and Causal Inference
3.4. Evaluation
4. Results
5. Discussion
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
Abbreviations
AUROC | Area Under the Receiver Operating Characteristic curve |
COVID-19 | Coronavirus Disease 2019 |
BN | Bayesian Network |
DAG | Directed Acyclic Graph |
LOCO | Leave-One-Country-Out |
NOTEARS | Non-combinatorial Optimization via Trace Exponential and Augmented lagRangian for |
Structure learning |
Appendix A
From | To |
---|---|
Population Over 65 (%) | |
Any node | Twitter Usage (%) |
Single Household (%) | |
Twitter Activity | Any node |
Sentiment | |
Total Infections | |
New Infections | |
Twitter Usage (%) | Change in Infections (%) |
Lockdown Announcement | Total Deaths |
New Deaths | |
Change in Deaths (%) | |
Population Over 65 (%) | Twitter Activity |
Single Household (%) | Sentiment |
Twitter Usage (%) | Sentiment |
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Cross Validation Test Country | AUROC |
---|---|
Austria | 0.798 |
Belgium | 0.728 |
Denmark | 0.831 |
France | 0.776 |
Germany | 0.992 |
Italy | 0.976 |
Netherlands | 0.746 |
Norway | 0.907 |
Spain | 0.766 |
Sweden | 0.998 |
Switzerland | 0.789 |
United Kingdom | 0.684 |
Average | 0.833 |
Query | Variable and State | |
---|---|---|
Single-person household (%) = H | Total Infections = H | 0.178 |
65+ (%) = L | ||
Single-person household (%) = L | Total Infections = H | 0.241 |
65+ (%) = H | ||
New Infections = H | Twitter Activity = H | 0.496 |
New Deaths = H | ||
New Infections = L | Twitter Activity = H | 0.184 |
New Deaths = L | ||
New Infections = H | ||
New Deaths = H | Twitter Activity = H | 0.800 |
Twitter Usage = H | ||
Lockdown Announcement = Yes | ||
New Infections = L | ||
New Deaths = L | Twitter Activity = H | 0.120 |
Twitter Usage = L | ||
Lockdown Announcement = No | ||
New Deaths = H | Sentiment = Neg | 0.624 |
New Deaths = L | Sentiment = Neg | 0.277 |
Total Deaths = H | Sentiment = Neg | 0.344 |
Total Deaths = L | Sentiment = Neg | 0.290 |
Lockdown Announcement = Yes | Sentiment = Neg | 0.501 |
Lockdown Announcement = No | Sentiment = Neg | 0.286 |
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Gencoglu, O.; Gruber, M. Causal Modeling of Twitter Activity during COVID-19. Computation 2020, 8, 85. https://doi.org/10.3390/computation8040085
Gencoglu O, Gruber M. Causal Modeling of Twitter Activity during COVID-19. Computation. 2020; 8(4):85. https://doi.org/10.3390/computation8040085
Chicago/Turabian StyleGencoglu, Oguzhan, and Mathias Gruber. 2020. "Causal Modeling of Twitter Activity during COVID-19" Computation 8, no. 4: 85. https://doi.org/10.3390/computation8040085
APA StyleGencoglu, O., & Gruber, M. (2020). Causal Modeling of Twitter Activity during COVID-19. Computation, 8(4), 85. https://doi.org/10.3390/computation8040085