Abstract
Early detection and treatment are regarded as the most effective ways to prevent suicidal ideation and potential suicide attempts—two critical risk factors resulting in successful suicides. Online communication channels are becoming a new way for people to express their suicidal tendencies. This paper presents an approach to understand suicidal ideation through online user-generated content with the goal of early detection via supervised learning. Analysing users’ language preferences and topic descriptions reveals rich knowledge that can be used as an early warning system for detecting suicidal tendencies. Suicidal individuals express strong negative feelings, anxiety, and hopelessness. Suicidal thoughts may involve family and friends. And topics they discuss cover both personal and social issues. To detect suicidal ideation, we extract several informative sets of features, including statistical, syntactic, linguistic, word embedding, and topic features, and we compare six classifiers, including four traditional supervised classifiers and two neural network models. An experimental study demonstrates the feasibility and practicability of the approach and provides benchmarks for the suicidal ideation detection on the active online platforms: Reddit SuicideWatch and Twitter.
Highlights
Suicide might be considered as one of the most serious social health problems in the modern society
We investigate the problem of suicidal ideation detection in online social websites, with a focus on understanding and detecting the suicidal thoughts in online user content
User-generated posts are varied in length, and some statistical features can be extracted from texts
Summary
Suicide might be considered as one of the most serious social health problems in the modern society. Six different sets of informative features were extracted and six supervised learning algorithms were compared to detect suicidal ideation within the data It is a novel application of automatic suicidal intention detection on social content with the combination of our proposed effective feature engineering and classification models. (1) Knowledge discovery: this is a novel application of knowledge discovery and data mining to detect suicidal ideation in online user content Previous work in this field has been conducted by psychological experts with statistical analysis; this approach reveals knowledge on suicidal ideation from a data analytic perspective.
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