The COVID-19 pandemic has escalated mental health crises worldwide, with social isolation
and economic instability contributing to a rise in suicidal behavior. Suicide can result from …

Social media platforms are increasingly being used for studying psycho-linguistic
phenomenon to model expressions of suicidal intent in tweets. Most recent work in suicidal …

In recent years, automated machine learning has assisted mental health researchers in
analyzing large amounts of data. Despite this, many individuals and organizations hesitate …

Suicide, the act of taking ones own life, is a tragedy for all involved and a public health
concern in the United States. Suicide is the tenth leading cause of death in the United States …

Abstract Machine learning analysis of social media data represents a promising way to
capture longitudinal environmental influences contributing to individual risk for suicidal …

Social media data that characterize users can provide mental health signals, including
suicide risks. Existing methods for suicide risk identification on social media have …

Recent studies have shown that social media has increasingly become a platform for users
to express suicidal thoughts outside traditional clinical settings. With advances in Natural …

Social media's ubiquity fosters a space for users to exhibit suicidal thoughts outside of
traditional clinical settings. Understanding the build-up of such ideation is critical for the …

Social media use continues to grow worldwide and an ever-growing number of people are
using various social media platforms to update their social circles on their mental health …

The identification of suicidal thoughts in online social networks is an expanding field of study
fraught with major challenges. Recent studies have shown that the readily available data …