Next Article in Journal
Biomass Steam Gasification: A Comparison of Syngas Composition between a 1-D MATLAB Kinetic Model and a 0-D Aspen Plus Quasi-Equilibrium Model
Next Article in Special Issue
An Accuracy vs. Complexity Comparison of Deep Learning Architectures for the Detection of COVID-19 Disease
Previous Article in Journal
Development of a Parallel 3D Navier–Stokes Solver for Sediment Transport Calculations in Channels
Previous Article in Special Issue
A Computational Study to Identify Potential Inhibitors of SARS-CoV-2 Main Protease (Mpro) from Eucalyptus Active Compounds

Causal Modeling of Twitter Activity during COVID-19

Faculty of Medicine and Health Technology, Tampere University, 33720 Tampere, Finland
LEO Pharma, 2750 Ballerup, Denmark
Author to whom correspondence should be addressed.
Computation 2020, 8(4), 85;
Received: 26 August 2020 / Revised: 22 September 2020 / Accepted: 25 September 2020 / Published: 29 September 2020
(This article belongs to the Special Issue Computation to Fight SARS-CoV-2 (CoVid-19))
Understanding the characteristics of public attention and sentiment is an essential prerequisite for appropriate crisis management during adverse health events. This is even more crucial during a pandemic such as COVID-19, as primary responsibility of risk management is not centralized to a single institution, but distributed across society. While numerous studies utilize Twitter data in descriptive or predictive context during COVID-19 pandemic, causal modeling of public attention has not been investigated. In this study, we propose a causal inference approach to discover and quantify causal relationships between pandemic characteristics (e.g., number of infections and deaths) and Twitter activity as well as public sentiment. Our results show that the proposed method can successfully capture the epidemiological domain knowledge and identify variables that affect public attention and sentiment. We believe our work contributes to the field of infodemiology by distinguishing events that correlate with public attention from events that cause public attention. View Full-Text
Keywords: Twitter; machine learning; causal inference; COVID-19; sentiment analysis; social media Twitter; machine learning; causal inference; COVID-19; sentiment analysis; social media
Show Figures

Figure 1

MDPI and ACS Style

Gencoglu, O.; Gruber, M. Causal Modeling of Twitter Activity during COVID-19. Computation 2020, 8, 85.

AMA Style

Gencoglu O, Gruber M. Causal Modeling of Twitter Activity during COVID-19. Computation. 2020; 8(4):85.

Chicago/Turabian Style

Gencoglu, Oguzhan, and Mathias Gruber. 2020. "Causal Modeling of Twitter Activity during COVID-19" Computation 8, no. 4: 85.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop