Detecting suicidality on Twitter

By Bridianne O'Dea, Stephen Wan, Philip J. Batterham, A. Calear, Cécile Paris, Helen Christensen. 2015

418 citations recorded by the source corpus.

This project was supported in part by funding from the NSW Mental Health Commission and the NHMRC John Cade Fellowship 1056964. PJB and ALC are supported by the NHMRC Early Career Fellowships 1035262 and 1013199.

Subfields: Clinical Psychology, Social Psychology

View Paper: https://www.semanticscholar.org/paper/2aa3cd61774eaa9a5ccc92212de5ec38c1f5206f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Clinical Psychology

1. Helen Christensen
2. Rafael A. Calvo
3. Erik Cambria

In Social Psychology

1. David N. Milne
2. Helen Christensen
3. Rafael A. Calvo
4. M. Hussain
5. Erik Cambria
6. Shaoxiong Ji

Citations in the corpus

• Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications (2019, methodology): “The research techniques used to examine suicide also span many fields and methods, for example, clinical methods with patient-clinic interaction [8] and automatic detection from user generated content (mainly text) [11], [16].”
• Natural language processing in mental health applications using non-clinical texts† (2017, background): “O’Dea et al. (2015) aim to separate out genuinely concerning tweets mentioning suicidal ideation from the large amount of flippancy and hyperbole that exists on the platform.”