Citation

Sharma N, Karwasra P. Proc. TENCON 2023; 25-30.

Copyright

(Copyright © 2023, IEEE Computer Society Conference Publishing Services)

DOI

10.1109/TENCON58879.2023.10322499

PMID

unavailable

Abstract

The advent of social media has transformed the way we communicate and connect, enabling individuals worldwide to instantly and openly interact with friends, family, and colleagues on a frequent basis. People utilize social media platforms as a means to express their opinions, share personal experiences, narratives, and challenges. Nevertheless, concerns have arisen due to the growing prevalence of suicidal content on social media platforms, where discussions of hardship, thoughts of death, and self-harm are widespread, particularly among younger generations. Consequently, harnessing the power of social media to detect and identify suicidal behavior, including the presence of suicidal thoughts, becomes essential in offering appropriate interventions that discourage self-harm and suicide, as well as in preventing the spread of suicidal ideations throughout these platforms. This paper presents suicidal content detection using two deep learning architectures, LSTM, and DistilBERT with the latter showing better performance in respectively. We conclude by drawing implications for deep learning architectures in detecting suicidal content on social media and an initial deployment of the models using Telegram bot which detects the message containing suicidal content and sends a motivational message in response and also informs their friends and relative through alerts.