Abstract
Digital data are abundantly available for researchers in the age of the Internet of Things. In the psychological and psychiatric sciences such data can be used in myriad ways to obtain insights into mental states and traits. Most importantly, such data allow researchers to record and analyze behavior in a real-world context, a scientific approach which was expensive and difficult to conduct until only recently. Much research in recent years linked digital footprints to self-report questionnaire data, likely to demonstrate proof of concept(s)—for instance linking socializing on the smartphone to self-reported extraversion (a personality trait linked to socializing)—in the sciences investigating the human mind. The present perspective piece reflects on this approach by revisiting recent work which has been carried out mining smartphone log and social media data and questions if and when self-report data will still be of relevance in psychological/psychiatric research in the near future.
Highlights
For several years, the psychological and psychiatric sciences have increasingly relied on the study of digital footprints to obtain insights into human nature [1,2,3,4]
Digital phenotyping describes the prediction of mental states and traits using digital footprints from the Internet of Things (IoT)
The research path probably most chosen at the moment to obtain insight into mental states/traits from digital footprints is to study digital data or traces such as smartphone log data or from social media and link them to self-report data assessing variables such as mood or personality [7,8,9,10]
Summary
The psychological and psychiatric sciences have increasingly relied on the study of digital footprints to obtain insights into human nature [1,2,3,4]. Keywords Digital phenotyping · Mobile sensing · Personality · Self-report · Digital footprints · Big data · Smartphone
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