2021
DOI: 10.2139/ssrn.3771695
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Infodemiology: Computational Methodologies for Quantifying and Visualizing Key Characteristics of the COVID-19 Infodemic

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… Show more

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Cited by 4 publications
(2 citation statements)
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“…6 Researchers have characterized the spread of rumors using an epidemiological framework, even estimating the "infectiousness" or pace of sharing particular pieces of information and looking at mediating factors such as linguistic features, message believability, individual personality, or group trust. [8][9][10] While, on the one hand, researchers have quantified rumor dynamics, public health professionals and policy makers have oversimplified rumors as simply myths or folklore to be corrected. 11 Stories characterized as "rumors" (such as rumors of abuse by humanitarian aid workers or secret testing of medical treatments using coercion or without consent) may later prove true and may have far-reaching impacts.…”
Section: Introductionmentioning
confidence: 99%
“…6 Researchers have characterized the spread of rumors using an epidemiological framework, even estimating the "infectiousness" or pace of sharing particular pieces of information and looking at mediating factors such as linguistic features, message believability, individual personality, or group trust. [8][9][10] While, on the one hand, researchers have quantified rumor dynamics, public health professionals and policy makers have oversimplified rumors as simply myths or folklore to be corrected. 11 Stories characterized as "rumors" (such as rumors of abuse by humanitarian aid workers or secret testing of medical treatments using coercion or without consent) may later prove true and may have far-reaching impacts.…”
Section: Introductionmentioning
confidence: 99%
“…Extracted using their native application programming interfaces (API) or third party services such as Crowdtangle (CrowdTangle Team, 2021) (Hussain, et al, 2020), data from Facebook and Twitter are rich sources of text and semantic data. When subjected to topic modeling and sentiment analysis, social media posts can provide statistical information on prevailing public discourse (Hamzah, Lau, Nazri, & Ligot, 2020), speed of dissemination, and related communication networks (Ligot D. , Tayco, Toledo, Nazareno, & Brennan-Rieder, 2021).…”
Section: Social Listeningmentioning
confidence: 99%