Wei Zeng
ABSTRACT
Suicide has become a global public health challenge. In the context of social media becoming an indispensable part of people's lives, early detection of social media users who may exhibit suicidal tendencies holds significant importance. With the aid of deep learning and natural language processing techniques, superior results have been achieved in identifying suicide risks compared to traditional machine learning methods. In this study, we conducted a suicide risk assessment and prediction on text data from the Reddit platform, proposing a neural network model based on BiLSTM and Attention mechanisms. Through testing on publicly available datasets, the proposed method achieved an accuracy of 94% and outperformed other models in terms of precision, recall, and F1 Score metrics.
- Mueller, Anna S., and Seth Abrutyn. "Suicidal disclosures among friends: using social network data to understand suicide contagion." Journal of health and social behavior 56.1. 2015: 131-148.Google Scholar
- Barak, Azy, and Ofra Miron. "Writing characteristics of suicidal people on the Internet: A psychological investigation of emerging social environments." Suicide and Life-Threatening Behavior 35.5. 2005: 507-524.Google Scholar
- Desmet, Bart, and Véronique Hoste. "Emotion detection in suicide notes." Expert Systems with Applications 40.16. 2013: 6351-6358.Google Scholar
- Vioules, M. Johnson, "Detection of suicide-related posts in Twitter data streams." IBM Journal of Research and Development 62.1. 2018: 7-1.Google Scholar
- O'dea, Bridianne, "Detecting suicidality on Twitter." Internet Interventions 2.2. 2015: 183-188.Google Scholar
- Du, Jingcheng, "Extracting psychiatric stressors for suicide from social media using deep learning." BMC medical informatics and decision making 18. 2018: 77-87.Google Scholar
- Tadesse, Michael Mesfin, "Detection of suicide ideation in social media forums using deep learning." Algorithms 13.1. 2019: 7.Google Scholar
- Ji, Shaoxiong, "Suicidal ideation and mental disorder detection with attentive relation networks." Neural Computing and Applications 34.13. 2022: 10309-10319.Google Scholar
- Cao, Lei, "Latent suicide risk detection on microblog via suicide-oriented word embeddings and layered attention." arXiv preprint arXiv:1910.12038. 2019.Google Scholar
- Kancharapu, Rohini, and Sri Nagesh A Ayyagari. "A comparative study on word embedding techniques for suicide prediction on COVID-19 tweets using deep learning models." International Journal of Information Technology. 2023: 1-14.Google Scholar
- Wang, Yequan, "Attention-based LSTM for aspect-level sentiment classification." Proceedings of the 2016 conference on empirical methods in natural language processing. 2016.Google Scholar
- Mikolov, Tomas, "Efficient estimation of word representations in vector space." arXiv preprint arXiv:1301.3781. 2013.Google Scholar
- Pennington, Jeffrey, Richard Socher, and Christopher D. Manning. "Glove: Global vectors for word representation." Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP). 2014.Google Scholar
- KOMATI. (2023.October). Suicide and Depression Detection, Version 14. Retrieved September 20, 2023 from https://www.kaggle.com/datasets/nikhileswarkomati/suicide-watch/data.Google Scholar
Index Terms
- Attention-based BiLSTM Network for Social Media Suicide Detection
Recommendations
Social media user classification: based on social capital expectation, susceptibility, and compulsion loop
ICEC '17: Proceedings of the International Conference on Electronic CommerceSocial media such as Facebook, Instagram and Twitter are originally developed as communication tools among individuals for private conversations. Through the platforms, people share photos, stories and news with their social media friends to interact ...
College students social media use and communication network heterogeneity
This study examined whether and how the usage of social media can influence college students' level of network heterogeneity and how network heterogeneity is associated with levels of bridging/bonding social capital and subjective well-being. In ...
Comments