Can analyzing social media posts detect anorexia? Concordia researchers say, ‘yes’
MONTREAL – Analyzing someone’s social media activity can determine whether or not they suffer from anorexia nervosa, an eating disorder, according to researchers from Concordia University.
The paper’s findings were revealed in September at the CLEF 2019 conference in Lugano, Switzerland.
Graduate students Elham Mohammadi and Hessam Amini looked at posts by Reddit users, which they labelled as “anorexic” or “non-anorexic.”
They then observed the users’ profiles and post histories as a whole.
“When these posts are seen in isolation, they may camouflage patterns [of] the potential presence of serious mental health issues,” the researchers explain.
“These can range from suicidal thoughts to post-traumatic stress to eating disorders.”
Mohammadi and Amini worked on the project with Leila Kosseim, a professor at the Department of Computer Science and Software Engineering at the Computational Linguistics at Concordia (CLaC) Laboratory.
Using what they call “deep learning algorithms,” the group looked for patterns that could identify a person as being anorexic or at risk of anorexia.
“By analyzing enough posts…the algorithms will be able to tell the difference between users who suffer from the eating disorder and those who do not,” the researchers stated, adding that the model proved to be “highly accurate.”
They say their goal is to detect the signs of harm as early as possible.
“Very often, timing is of the essence,” said Mohammadi. “In suicidal ideation, for instance, we don’t want to detect that after the fact.”
The hope is to also identify people seeking help for friends and loved ones, as well as psychologists and other mental health professionals who can reach out to those in need.
“It is becoming more urgent to identify people suffering from anorexia because studies are suggesting that the number of people in need of treatment is much higher than the number of people receiving it,” said Amini.
The group says they chose to use a computer algorithm because it was simply more efficient than having a person monitor multiple posts and online users at once.
“Detecting posts that suggest anorexia is like trying to find a needle in a haystack,” Kosseim said, pointing out that similar systems have been designed to detect cyberbullying and hate speech on social media.
“If you have a machine that monitors and identifies the needle every once in a while, these posts can be forwarded to a mental health professional.”