Toward developing adolescent-centered machine learning methods to detect depression: Interviews with Latino adolescents to identify signals of emotional and somatic symptoms within social media data.

Abstract

Despite rising use of machine learning (ML) methods to detect depression within social media data, few are developed with and for adolescents. This is unfortunate, because adolescents may be more likely than adults to experience somatic than emotional symptoms and may be less likely to express emotions on social media. Accordingly, ML methods that focus on emotional symptoms may undercount adolescents at risk for depression. As a step toward developing an adolescent-centered ML method, we co-developed an interview guide with Latino adolescents to understand 1) social media norms for expressing somatic and emotional symptoms; and 2) identify potential signals of each. For the latter, we adopted a novel approach of asking interviewees to take on the "human classifier" role and tell us what they look for within social media data. Using framework analysis on 43 interviews with Latino adolescents, we find evidence suggesting norms prescribe more strongly against conveying emotional symptoms than somatic symptoms on social media. Additionally, rather than literal statements conveying they are experiencing depression, adolescents appear to use audiovisual cues to signal emotional symptoms and posting behavior (time of post, posting less) for somatic symptoms. Accordingly, norms may hinder opportunities for leveraging social media data to detect depression among adolescents, particularly when using ML methods that search for literal statements of depression or signals of emotional symptoms. Because peers tend to recognize depression in an adolescent earlier than medical experts, these findings suggest the need to develop and validate ML methods that incorporate a set of signals for somatic symptoms, particularly audiovisual cues and posting behavior. We discuss the benefits of "centering at the margins," which is focusing on a population that is understudied within this domain, and the need for ML methods developed with adolescent input.