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COVID ‐19 pandemic and information diffusion analysis on Twitter
Author(s) -
Dinh Ly,
Parulian Nikolaus
Publication year - 2020
Publication title -
proceedings of the association for information science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.193
H-Index - 14
ISSN - 2373-9231
DOI - 10.1002/pra2.252
Subject(s) - diffusion , social media , pandemic , covid-19 , diffusion theory , computer science , social network analysis , information dissemination , social distance , information cascade , data science , innovation diffusion , world wide web , psychology , social psychology , medicine , physics , knowledge management , infectious disease (medical specialty) , disease , pathology , thermodynamics
The COVID‐19 pandemic has impacted all aspects of our lives, including the information spread on social media. Prior literature has found that information diffusion dynamics on social networks mirror that of a virus, but applying the epidemic Susceptible‐Infected‐Removed model (SIR) model to examine how information spread is not sufficient to claim that information spreads like a virus. In this study, we explore whether there are similarities in the simulated SIR model ( SIRsim ), observed SIR model based on actual COVID‐19 cases ( SIRemp ), and observed information cascades on Twitter about the virus ( INFOcas ) by using network analysis and diffusion modeling. We propose three primary research questions: (a) What are the diffusion patterns of COVID‐19 virus spread, based on SIRsim and SIRemp ? (b) What are the diffusion patterns of information cascades on Twitter ( INFOcas ), with respect to retweets, quote tweets, and replies? and (c) What are the major differences in diffusion patterns between SIRsim , SIRemp , and INFOcas ? Our study makes a contribution to the information sciences community by showing how epidemic modeling of virus and information diffusion analysis of online social media are distinct but interrelated concepts.