Infoveillance and Social Listening
"Infoveillance is important for both the supply and demand sides. Public health professionals want to know, for example, if there is a surge of misinformation on the Internet on vaccination, so that public health campaigns and “health marketing” efforts can effectively counterbalance the misinformation. Public health professionals also need to know about surges in information demand, be it to address “epidemics of fear” by supplying the public with appropriate information, or to detect real disease outbreaks for which spikes in Internet searches or chatter in newsgroups and postings on microblogs (Twitter etc) may be an early predictor." [Eysenbach 2009]
The term "social listening" is sometimes used as a synonym for infoveillance, but has also been more narrowly defined as "the process of identifying and assessing what is being said about a company, product, brand, or individual, within forms of electronic interactive media" [Anderson et al, 2017]. The term "social listening" was first used in the public health context in 2015 by Heather Cole-Lewis when tracking sentiments on e-cigarettes on Twitter [Cole-Lewis et al, 2015].
See also related E-Collections:
Assessing and Building eHealth / Digital Literacy in Populations
Training for Infodemic Managers and Public Health Professionals
Misinformation and Disinformation Outbreaks and Information Prevalence Studies
Information prevalence studies measure the absolute or relative number of occurrences of a certain keyword or concept (eg, misinformation or facts) in a pool of information [Eysenbach 2009]. These kinds of studies are particularly useful if we track them longitudinally (ie, track how the number of internet postings on a given health-specific topic changes over time), as we would, for example, to see changes in relation to certain external events, such as a media campaign or a disease outbreak.
With demand-based infodemiology indicators we usually refer to data generated from the search and “click” (ie, navigation) behavior of people. [Eysenbach 2009]
Health and Risk Communication
Policy for Infodemiology and Infodemic Management
Equity Issues in Information Distribution
Vaccination Sentiment and Anti-Vaccination Infodemiology
Data Sources and Open Data for Infodemiology
Bots and AI Approaches to Detect and Counter Misinformation
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