A data collection and computational framework that includes precise recording and mapping of fragmented digital lives.
Mapping the human screenome can be a critical and cross-cutting part of solutions and theories about social challenges involving media – from fake news to smartphone addiction to social media and mental health.
This video shows a sample movie of one person’s smartphone use for 3 minutes. Every 5 seconds that the phone screen is activated, a screenshot is recorded, compressed, encrypted and transmitted to secure servers at the Human Screenome Project at Stanford University. The movie shows a compilation of screens that represents 15 mins of use over approximately 2 hours of one day. The movie demonstrates that digital content is diverse and fragmented, with different content threaded into sequences that break apart traditional message (e.g., videos, news stories, conversations) but make sense to individual users.
An analysis of smartphone usage by two adolescents over one month shows what can be learned from a screenome analysis that cannot be learned from traditional surveys of media use.
Smartphone screenomes for two adolescents over 21 days are illustrated in the figure. The top half of the figure shows Participant A, the bottom half Participant B. Each row represents a day, starting from 6 am on the left and going to midnight on the right. Smartphone use from midnight to 5:59am has been deleted for this illustration. The vertical bars indicate whether the smartphone screen was on during each 5-second interval of each day, and the type of application that was engaged during each interval. Applications are coded into 10 categories:
The data in the two large panels highlight how smartphone use varies substantially between the two people, and between days and hours within each person. For example, Participant A on the top had more and shorter sessions, and spent more time on social media (red lines). Participant B on the bottom had fewer and longer sessions and spent more time watching video (purple lines).
The two larger horizontal lines “zoom in” on (magnify) a 2-hour period at the end of a day for each participant, starting at 10 pm on the left and going to midnight on the right. The white vertical lines indicate screens where the participant was generating content (e.g., composing a text message, posting on social media). For Participant A, there was quick switching between different types of applications in the first 15 minutes followed by and extended period using social media (red) and game play (green), followed at the end of the night by quick switching between social and several other types of content. For Participant B, there were extended periods watching video (purple) and playing games (green) in the first 90 minutes, followed by quick switching mixed with substantial creation of content in the last 30 minutes. The comparison between these two participants is also summarized in Reeves, et al., Nature, January 2020 (in press). A more complete discussion of adolescent smartphone data can be found in Ram, et al., Journal of Adolescent Research, 2019.
We have already begun to demonstrate diverse applications of the screenome:
In politics, we found that the screenome shows close links between personal messaging and interpretations of news (Muise, et al., 2017).
In medicine, we showed that the screenome contains the presence of drug and disease-related signals for diabetes (Gijsen, et al., 2019).
A comparison of screenomes gathered in the US, China and Myanmar showed that the number of smartphone sessions differed substantially across countries while the structure of individual sessions was quite similar (Muise, et al., 2019).
And in comparisons of adolescent screenomes we showed extremely quick switching among highly varied content and idiosyncratic preoccupation with specialized content, patterns that have substantial implications for health, development and well-being (Ram, et al., 2019).
Why it’s hot:
It’s hot because analyses like this can potentially surface insights that cannot be learned from traditional surveys of media use. Identifying specific sequences of smartphone activity could be important to understanding how people actually use their digital devices to communicate and lead their off-screen lives.