Hierarchical Multiscale Recurrent Neural Networks for Detecting Suicide Notes

Schoene, Annika Marie; Turner, Alexander P.; De Mel, Geeth; Dethlefs, Nina

Abstract

Recent statistics in suicide prevention show that people are increasingly posting their last words online and with the unprecedented availability of textual data from social media platforms researchers have the opportunity to analyse such data. Furthermore, psychological studies have shown that our state of mind can manifest itself in the linguistic features we use to communicate. In this article, we investigate whether it is possible to automatically identify suicide notes from other types of social media blogs in two document-level classification tasks. The first task aims to identify suicide notes from depressed and blog posts in a balanced dataset, whilst the second experiment looks at how well suicide notes can be classified when there is a vast amount of neutral text data, which makes the task more applicable to real-world scenarios. Furthermore, we perform a linguistic analysis using LIWC (Linguistic Inquiry and Word Count). We present a learning model for modelling long sequences in two experiment series. We achieve an f1-score of 88.26 percent over the baselines of 0.60 in experiment 1 and 96.1 percent over the baseline in experiment 2. Finally, we show through visualisations which features the learning model identifies, these include emotions such as love and personal pronouns.

Citation

Schoene, A. M., Turner, A. P., De Mel, G., & Dethlefs, N. (2023). Hierarchical Multiscale Recurrent Neural Networks for Detecting Suicide Notes. IEEE Transactions on Affective Computing, 14(1), 153-164. https://doi.org/10.1109/TAFFC.2021.3057105

• Journal Article Type: Article
• Acceptance Date: Jan 19, 2021
• Online Publication Date: Feb 5, 2021
• Publication Date: 2023-01
• Deposit Date: Sep 13, 2024
• Print ISSN: 1949-3045
• Electronic ISSN: 1949-3045
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 14
• Issue: 1
• Pages: 153-164
• DOI: https://doi.org/10.1109/TAFFC.2021.3057105
• Public URL: https://nottingham-repository.worktribe.com/output/25531588
• Publisher URL: https://ieeexplore.ieee.org/document/9349170