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Hierarchical multiscale recurrent neural networks for detecting suicide notes

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journal contribution
posted on 2025-05-08, 11:31 authored by Annika Marie Schoene, Alexander P Turner, Geeth De Mel, Nina DethlefsNina Dethlefs
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.

Funding

Partly supported by the STFC Hartree Cen?tre’s Innovation Return on Research programme, funded by the Department for Business, Energy Industrial Strategy.

History

School

  • Science

Published in

IEEE Transactions on Affective Computing

Volume

14

Issue

1

Pages

153 - 164

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Version

  • AM (Accepted Manuscript)

Rights holder

© IEEE

Publisher statement

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Acceptance date

2021-01-23

Publication date

2021-02-05

Copyright date

2021

eISSN

1949-3045

Language

  • en

Depositor

Prof Nina Dethlefs. Deposit date: 22 April 2025