Detecting Potentially Harmful and Protective Suicide-Related Content on Twitter: Machine Learning Approach.

ABSTRACT:

Background

Research has repeatedly shown that exposure to suicide-related news media content is associated with suicide rates, with some content characteristics likely having harmful and others potentially protective effects. Although good evidence exists for a few selected characteristics, systematic and large-scale investigations are lacking. Moreover, the growing importance of social media, particularly among young adults, calls for studies on the effects of the content posted on these platforms.

Objective

This study applies natural language processing and machine learning methods to classify large quantities of social media data according to characteristics identified as potentially harmful or beneficial in media effects research on suicide and prevention.

Methods

SUBMITTER: Metzler H 

PROVIDER: S-EPMC9434391 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature