Researchers showed long ago that artificial intelligence models could identify a person's basic psychological traits from their digital footprints in social media.
A research team from the Stanford University and the University of Michigan found that machine-learning models can be used to map a person's mood swings and volatility during a certain period of time.
According to the Phys.org website, the researchers used language processing tools to monitor users' digital footprint, and determine whether a person is happy or sad at any given time.
Using this algorithm, they also managed to produce a video out of a person's emotional ups and downs during a given period.
The findings could spark new worries about privacy, but researcher Johannes Eichstaedt from the University of Stanford says the approach could help diagnose people with mood disorders and see how well they respond to different types of therapy.
"If this kind of approach is used ethically and legally, with strict privacy protection, we could someday have ways to computationally understand the mind. It could help with diagnosis and pharmaceutical evaluation. It could also help us track the psychological impact of traumatic societal events, such as the COVID pandemic," Eichstaedt says.
The team analyzed the posts of nearly 3,000 volunteers to determine the mood expressed by the posts, and whether they can be described as positive or negative. Then, the posts were used to train a machine-learning model to produce a dataset that helps evaluate similar posts on social media.
