Unveiling the Hidden Truth of Drug Addiction: A Social Media Approach Using Similarity Network-Based Deep Learning

Substance use and sentiment and topical tendencies: a study using social media conversations of youth experiencing homelessness

Purpose

This study investigates associations between Facebook (FB) conversations and self-reports of substance use among youth experiencing homelessness (YEH). YEH engage in high rates of substance use and are often difficult to reach, for both research and interventions. Social media sites provide rich digital trace data for observing the social context of YEH's health behaviors. The authors aim to investigate the feasibility of using these big data and text mining techniques as a supplement to self-report surveys in detecting and understanding YEH attitudes and engagement in substance use.

Design/methodology/approach

Participants took a self-report survey in addition to providing consent for researchers to download their Facebook feed data retrospectively. The authors collected survey responses from 92 participants and retrieved 33,204 textual Facebook conversations. The authors performed text mining analysis and statistical analysis including ANOVA and logistic regression to examine the relationship between YEH's Facebook conversations and their substance use.

Findings

Facebook posts of YEH have a moderately positive sentiment. YEH substance users and non-users differed in their Facebook posts regarding: (1) overall sentiment and (2) topics discussed. Logistic regressions show that more positive sentiment in a respondent's FB conversation suggests a lower likelihood of marijuana usage. On the other hand, discussing money-related topics in the conversation increases YEH's likelihood of marijuana use.

Originality/value

Digital trace data on social media sites represent a vast source of ecological data. This study demonstrates the feasibility of using such data from a hard-to-reach population to gain unique insights into YEH's health behaviors. The authors provide a text-mining-based toolkit for analyzing social media data for interpretation by experts from a variety of domains.