Logistic regression analysis of textual data on suicidal ideation

Suicide prevention requires careful consideration of the entire process, including ideation, attempt, and death. Understanding factors related to suicidal ideation is particularly critical for early intervention and effective prevention measures. In this study, we identified such factors by applying logistic regression analysis to textual data posted in September 2024 on NHK's "Facing Suicide" website, where individuals shared their thoughts and feelings. Several keywords, including "self/identity," were grouped into five categories, while demographic attributes such as gender, age, day of the week, and time of day were also examined. This analysis found that demographic and temporal factors influenced message content. Women and younger individuals were more likely to post messages centred on "self/identity" and "functional words/actions," suggesting that concerns related to self-perception and existence became more pronounced during late-night hours. This temporal pattern indicates that nighttime may serve as a critical period for heightened suicidal ideation.