Infodemiology: Computational Methodologies for Quantifying and Visualizing Key Characteristics of the COVID-19 Infodemic

22 Pages Posted: 26 Jan 2021 Last revised: 13 Feb 2021

See all articles by Dominic Ligot

Dominic Ligot

CirroLytix Research Services

Frances Claire Tayco

Independent

Mark Toledo

Cirrolytix

Carlos Nazareno

Independent

Denise Brennan-Rieder

CirroLytix Research Services

Date Written: January 23, 2021

Abstract

Objectives. Infodemics of false information on social media is a growing societal problem, aggravated by the occurrence of the COVID-19 pandemic. The development of infodemics has characteristic resemblances to epidemics of infectious diseases. This paper presents several methodologies which aim to measure the extent and development of infodemics through the lens of epidemiology.

Methods. Time varying R was used as a measure for the infectiousness of the infodemic, topic modeling was used to create topic clouds and topic similarity heat maps, while network analysis was used to create directed and undirected graphs to identify super-spreader and multiple carrier communities on social media.

Results. Forty-two (42) latent topics were discovered. Reproductive trends for a specific topic were observed to have significantly higher peaks (Rt 4-5) than general misinformation (Rt 1-3). From a sample of social media misinformation posts, a total of 385 groups and 804 connections were found within the network, with the largest group having 1,643 shares and 1,063,579 interactions over a 12 month period.

Conclusions. These approaches enable the measurement of the infectiousness of an infodemic, comparative analysis of infodemic topics, and identification of likely super-spreaders and multiple carriers on social media. The results of these analyses can form the basis for taking action to stem an ongoing spread of misinformation on social media and mitigate against future infodemics. The methods are not confined to health misinformation and may be applied to other infodemics, such as conspiracy theories, political disinformation, and climate change denial.

Keywords: COVID-19, Data Science, Reproductive Number, Social Networking, SARS-CoV-2

JEL Classification: C63, O33

Suggested Citation

Ligot, Dominic and Tayco, Frances Claire and Toledo, Mark and Nazareno, Carlos and Brennan-Rieder, Denise, Infodemiology: Computational Methodologies for Quantifying and Visualizing Key Characteristics of the COVID-19 Infodemic (January 23, 2021). Available at SSRN: https://ssrn.com/abstract=3771695 or http://dx.doi.org/10.2139/ssrn.3771695

Dominic Ligot (Contact Author)

CirroLytix Research Services ( email )

Pasig City
Philippines

HOME PAGE: http://cirrolytix.com

Frances Claire Tayco

Independent ( email )

Mark Toledo

Cirrolytix ( email )

Pasig City
Philippines

Carlos Nazareno

Independent ( email )

Denise Brennan-Rieder

CirroLytix Research Services ( email )

Pasig City
Philippines

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