Breaking the silence: leveraging social interaction data to identify high-risk suicide users online using network analysis and machine learning.

ABSTRACT: Suicidal thought and behavior (STB) is highly stigmatized and taboo. Prone to censorship, yet pervasive online, STB risk detection may be improved through development of uniquely insightful digital markers. Focusing on Sanctioned Suicide, an online pro-choice suicide forum, this work derived 17 egocentric network features to capture dynamics of social interaction and engagement within this uniquely uncensored community. Using network data generated from over 3.2 million unique interactions of N = 192 individuals, n = 48 of which were determined to be highest risk users (HRUs), a machine learning classification model was trained, validated, and tested to predict HRU status. Model prediction dynamics were analyzed using introspection techniques to uncover patterns in feature influence and hi

SUBMITTER: Lekkas D 

PROVIDER: S-EPMC11339441 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature