Analyzing Multiple Data Sources for Suicide Risk Detection: A Deep Learning Hybrid Approach

In the current digital landscape, social media’s extensive user-generated content presents a unique opportunity for identifying emotional distress signals. With suicide rates on the rise, this study takes aid of Natural Language Processing (NLP) and Sentiment Analysis to detect suicide risk. Centering primarily around deep learning (DL) architectures, including Convolutional Neural Network (CNN), Bidirectional Gated Recurrent Unit (Bi-GRU) and their combined hybrid BiGRU-CNN model, the research incorporates machine learning (ML) for comparative analysis through multisource datasets from Reddit and Twitter. The methodology commenced with data pre-processing, followed by exploring word embedding techniques. This research included an analysis of both Word2Vec variants as well as pretrained GloVe embeddings, where Skip-Gram paired with Adam optimizer showed superior results. For thorough evaluation, Receiver Operating Characteristic (ROC) curves, Confusion Matrix and Accuracy-Loss graphs were utilized. Furthermore, generalizability of employed models was testified and evaluated by in-depth inspections. The process was accomplished by activating manual input test, cross dataset test and k-fold cross validation procedures. In the course of scrutinizing, the proposed BiGRU-CNN model outperformed the traditional DL and ML models with consistent and reliable performance. Correspondingly, the proposed model achieved accuracies of 93.07% and 92.47% on the respective datasets which advocate its potential as a tool for the early detection of suicidal thought.

@article{Anika2024,
  title     = {Analyzing Multiple Data Sources for Suicide Risk Detection: A Deep Learning Hybrid Approach},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {2},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Saraf Anika and Swarup Dewanjee and Sidratul Muntaha},
  doi       = {10.14569/IJACSA.2024.0150270},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150270}
}