The algorithm categorises participants, who voluntarily work on recovery, into smaller groups, or subgroups, in a way that maintains helpful social connections and breaks social connections that could be detrimental to recovery and inadvertently expose them to negative behaviours.
"We know that substance abuse is highly affected by social influence; in other words, who you are friends with," said Aida Rahmattalabi, a post-doctoral student at the University of Southern California in the US.
"In order to improve the effectiveness of interventions, you need to know how people will influence each other in a group," Rahmattalabi added.
While group therapy can offer support to homeless youth, if not structured properly, they can also lead to friendships based on anti-social behaviour.
The team tackled this problem from an AI perspective, creating an algorithm that takes into account both how the individuals in a subgroup are connected -- their social ties -- and their prior history of substance abuse.
Survey data gathered voluntarily from homeless youth, as well as behavioural theories and observations of previous interventions, were used to build a computational model of the interventions.
"Based on this we have an influence model that explains how likely it is for an individual to adopt negative behaviours or change negative behaviours based on their participation in the group," Rahmattalabi noted.
"This helps us predict what happens when we group people into smaller groups," she said.
(This story has not been edited by Social News XYZ staff and is auto-generated from a syndicated feed.)
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