Researchers Advocate for Deeper Analysis of Online Habits

Determining the impact of screen time isn’t easy. It’s almost impossible to put together a “control group” of people living non-digital lives, and there are no baselines for such factors as “average daily Facebook usage.” Stanford University professor of communication Byron Reeves, in a paper in Human-Computer Interaction, suggested a new approach that eschews the term “screen time” as hopelessly ambiguous. Instead, he argued, scientists should analyze what people are watching — but this data doesn’t exist.

The New York Times reports that “researchers have linked daily time spent on specific platforms, like Facebook, to measures of well-being and mental health … but to build a more compelling understanding of the effects of digital experience, they’ll need … [to] record everything, on every device, that an individual sees, does, and types.” These researchers dub the result a “screenome,” which is different for every individual.

Reeves, whose paper included researchers from Penn State University, Boston University, Apple and Toyota Research Institute, noted, “the point is, your thread is yours, mine is mine, and we use it to regulate our emotions, to balance facts with fun, in our own idiosyncratic way.”

These researchers presented such digital threads: screenshots taken every few minutes, from several dozen people who had consented to such recordings. The threads showed that “people switched from one screen activity to another continually, every 20 seconds on average, and rarely spent more than 20 minutes uninterrupted on any one activity, even a full-length movie.”

The paper also showed “the digital threads of 30 college students, monitored over four days, [which] revealed wide differences in what people used their screens for, as well as in their patterns of switching from one kind of activity, like email, to another.”

What’s more complicated is to study “how these shifting patterns shape daily experience.” University of Pennsylvania’s Johannes Eichstaedt led researchers studying “the Facebook activity of 114 people diagnosed with depression … [and], while small by big-data standards, was the first to link to diagnoses in medical records, and … solidified previous correlations between online language content and low moods.”

“This is a well-documented process, that suffering generally contracts focus on the self, whereas mental well-being extends focus beyond the self,” said Eichstaedt.

From there, the researchers found that analyzing Facebook language “could predict whether a person was on their way to being diagnosed with depression about 70 percent of the time,” which Eichstaedt noted is “about the rate you get with clinical questionnaires.” NYT suggests that “incorporating screenomes from even a sample of people who became depressed would put the Facebook data in a far richer context, and possibly clarify whether online experience indeed lowered people’s moods — and why.”

For now, NYT continues, “screenome analysis may appeal primarily to people drawn to biotechnological self-discovery.” But for researchers, “asking which patterns of screenome activity are problematic, and for whom, is the better inquiry for today.”