Abstract
Suicide ideation prediction received attention in public health because it affects not only the victim but the general public. Identifying suicide ideation can be traditionally tricky and time-consuming, and the victim might have harmed himself or herself before detection. Easy access to social media provided textual data for Deep Learning (DL) models like Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) for the identification of suicide ideation. These models are more promising at identifying suicide ideation than traditional Machine Learning models because they can be applied to every form of datasets. The main challenge is combining the DL models and word embeddings. This research employed the DL model by combining CNN with bidirectional LSTM and two-word embedding techniques, FastText and GloVe (global vectors for word representation). The research questions are as follows: Will there be a difference in F1 score between two-word embeddings when combined with CNN-BiLSTM? And will the overfitting issue in the existing study be addressed? Using an existing study as a baseline, experimentation was done using a secondary Reddit dataset of 232,074 posts, achieving a 94% F1-score for FastText and GloVe. It was observed that GloVe could do better without approximating the F1-score and FastText when the running time is considered.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ahn, S.Y., Yu, S., Kim, J.E., Song, I.H.: The relationship between suicide bereavement and suicide ideation: analysis of the mediating effect of complicated grief. J. Affect. Disord. 331, 43–49 (2023). https://doi.org/10.1016/J.JAD.2023.03.008
Aldhyani, T.H.H., Alsubari, S.N., Alshebami, A.S., Alkahtani, H., Ahmed, Z.A.T.: Detecting and analyzing suicidal ideation on social media using deep learning and machine learning models. Int. J. Environ. Res. Public Health 19(19), 1–16 (2022). https://doi.org/10.3390/ijerph191912635
Davidson, J.E., et al.: Job-Related problems prior to nurse suicide, 2003–2017: a mixed methods analysis using natural language processing and thematic analysis. J. Nurs. Regul. 12(1), 28–39 (2021). https://doi.org/10.1016/S2155-8256(21)00017-X
Dong, L., Kalesnikava, V.A., Gonzalez, R., Mezuk, B.: Beyond depression: estimating 12-months prevalence of passive suicidal ideation in mid- and late-life in the health and retirement study. Am. J. Geriatr. Psychiatry 27(12), 1399–1410 (2019). https://doi.org/10.1016/j.jagp.2019.06.015
Fartacek, C., Kunrath, S., Aichhorn, W., Plöderl, M.: Therapeutic alliance and change in suicide ideation among psychiatric inpatients at risk for suicide. J. Affect. Disord. 323, 793–798 (2023). https://doi.org/10.1016/J.JAD.2022.12.028
Ghosh, S., Roy, S., Ekbal, A., Bhattacharyya, P.: CARES: CAuse recognition for emotion in suicide notes. In: Hagen, M., et al. (eds.) ECIR 2022. LNCS, vol. 13186, pp. 128–136. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-99739-7_15
Huang, Y., et al.: Comparison of three machine learning models to predict suicidal ideation and depression among Chinese adolescents: a cross-sectional study. J. Affect. Disord. 319, 221–228 (2022). https://doi.org/10.1016/J.JAD.2022.08.123
Ji, S., Pan, S., Li, X., Cambria, E., Long, G., Huang, Z.: Suicidal ideation detection: a review of machine learning methods and applications. IEEE Trans. Comput. Soc. Syst. 8(1), 214–226 (2021). https://doi.org/10.1109/TCSS.2020.3021467
Kang, C., et al.: Prevalence, risk factors and clinical correlates of suicidal ideation in adolescent patients with depression in a large sample of Chinese. J. Affect. Disord. 290, 272–278 (2021). https://doi.org/10.1016/j.jad.2021.04.073
Lauderdale, S.A., Martin, K.J., Oakes, K.R., Moore, J.M., Balotti, R.J.: Pragmatic screening of anxiety, depression, suicidal ideation, and substance misuse in older adults. Cogn. Behav. Pract. 29(1), 105–127 (2022). https://doi.org/10.1016/J.CBPRA.2021.06.003
Malviya, K., Roy, B., Saritha, S.: A transformers approach to detect depression in social media. In: 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), pp. 718–723 (2021). https://doi.org/10.1109/ICAIS50930.2021.9395943
NIH: Frequently Asked Questions About Suicide (2023)
Rebecca, A.: Nigeria has highest suicide rate in Africa, sixth globally. International Centre for Investigative Reporting (2019)
Sinha, P.P., Mahata, D., Mishra, R., Shah, R.R., Sawhney, R., Liu, H.: #suicidal - a multipronged approach to identify and explore suicidal ideation in twitter. In: International Conference on Information and Knowledge Management, Proceedings, pp. 941–950 (2019). https://doi.org/10.1145/3357384.3358060
WHO, et al.: World Health Organization. Department of Mental Health and Substance Abuse. Preventing Suicide: a Global Imperative. World Health Organization (2014)
Joulin, A., Grave, E., Bojanowski, P., Douze, M., Jégou, H., Mikolov, T.: Fasttext.zip: compressing text classification models. arXiv preprint arXiv:1612.03651 (2016)
Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543, October 2014
O’Shea, K., Nash, R.: An introduction to convolutional neural networks (2015). arXiv preprint arXiv:1511.08458
Huang, Z., Xu, W., Yu, K.: Bidirectional LSTM-CRF models for sequence tagging (2015). arXiv preprint arXiv:1508.01991
Lin, L., Chen, X., Shen, Y., Zhang, L.: Towards automatic depression detection: A BiLSTM/1D CNN-based model. Appl. Sci. 10(23), 8701 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Oyewale, C.T., Ibitoye, A.O.J., Akinyemi, J.D., Onifade, O.F.W. (2024). Suicide Ideation Prediction Through Deep Learning: An Integration of CNN and Bidirectional LSTM with Word Embeddings. In: Arai, K. (eds) Intelligent Computing. SAI 2024. Lecture Notes in Networks and Systems, vol 1017. Springer, Cham. https://doi.org/10.1007/978-3-031-62277-9_16
Download citation
DOI: https://doi.org/10.1007/978-3-031-62277-9_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-62276-2
Online ISBN: 978-3-031-62277-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)