Deep Learning -Based Suicidal Thought Detecion From Social Media With IOT-Enabled Alert System
Authors: J. Jenitta, Amulya N, Keerthi Kandakur, Nithin N, Shashank G
Abstract: As the online socialization process advances, more and more individuals are reporting their emotional conditions online and in many instances showing symptoms of misery and suicidal tendencies, which present significant obstacles to monitoring mental health. Conventional techniques of detecting such content are often untimely and ineffective and it is based on this need that automated and real time detection is important. In this paper, the author suggests a deep learning-based suicide risk prediction system based on linguistic data obtained on social media. The model achieves a high degree of accuracy in differentiating between texts that are suicidal and those that are not by learning the contextual semantics using bidirectional long short-term memory (Bi-LSTM) networks. The system is coupled with an IoT-based alert system to make sure that timely intervention is applied to prevent suicidal intent by sending alarms to caregivers or other interested parties whenever any suicidal intent is identified. The results of the testing show that the proposed method performs better in comparison to the traditional machine learning classifiers that are measured in terms of precision, recall, and F1-score. This combination related to the higher-order NLP techniques and the use of IoT-driven indicators offers a scalable and productive approach to stopping destructive online behavior. This article highlights the role of artificial intelligence and IoT in the improvement of digital mental health support systems.
DOI: http://doi.org/10.5281/zenodo.17590356
