Ideation

World Wide Web

Data stream mining

Creativity

Engineering

Transport engineering

Biology

Discriminative model

Identification (biology)

Suicide prevention

Social psychology

Computer security

Warning signs

Originality

Telecommunications

Data mining

Computer science

Botany

Environmental health

Cognitive science

Computer network

Internet privacy

Poison control

Psychology

STREAMS

Social media

Warning system

Data science

Artificial intelligence

Medicine

Sentiment analysis

Suicidal ideation

Machine learning

IBM Journal of Research and Development

Suicidal ideation detection in online social networks is an emerging research area with major challenges. Recent research has shown that the publicly available information, spread across social media platforms, holds valuable indicators for effectively detecting individuals with suicidal intentions. The key challenge of suicide prevention is understanding and detecting the complex risk factors and warning signs that may precipitate the event. In this paper, we present a new approach that uses the social media platform Twitter to quantify suicide warning signs for individuals and to detect posts containing suicide-related content. The main originality of this approach is the automatic identification of sudden changes in a user's online behavior. To detect such changes, we combine natural language processing techniques to aggregate behavioral and textual features and pass these features through a martingale framework, which is widely used for change detection in data streams. Experiments show that our text-scoring approach effectively captures warning signs in text compared to traditional machine learning classifiers. Additionally, the application of the martingale framework highlights changes in online behavior and shows promise for detecting behavioral changes in at-risk individuals.

Detection of suicide-related posts in Twitter data streams