Am I No Good? Towards Detecting Perceived Burdensomeness and Thwarted Belongingness from Suicide Notes
This work addresses suicide prevention by enabling automated detection of risk factors from notes, though it is incremental as it builds on existing datasets and methods.
The paper tackles the detection of two interpersonal suicide risk factors, Perceived Burdensomeness and Thwarted Belongingness, from suicide notes by introducing a multitask system and a new code-mixed dataset, achieving improved performance through the use of temporal and emotional information.
The World Health Organization (WHO) has emphasized the importance of significantly accelerating suicide prevention efforts to fulfill the United Nations' Sustainable Development Goal (SDG) objective of 2030. In this paper, we present an end-to-end multitask system to address a novel task of detection of two interpersonal risk factors of suicide, Perceived Burdensomeness (PB) and Thwarted Belongingness (TB) from suicide notes. We also introduce a manually translated code-mixed suicide notes corpus, CoMCEASE-v2.0, based on the benchmark CEASE-v2.0 dataset, annotated with temporal orientation, PB and TB labels. We exploit the temporal orientation and emotion information in the suicide notes to boost overall performance. For comprehensive evaluation of our proposed method, we compare it to several state-of-the-art approaches on the existing CEASE-v2.0 dataset and the newly announced CoMCEASE-v2.0 dataset. Empirical evaluation suggests that temporal and emotional information can substantially improve the detection of PB and TB.