Data Quality Matters: Suicide Intention Detection on Social Media Posts Using a RoBERTa-CNN Model

• URL: http://arxiv.org/abs/2402.02262v1
• Date: Sat, 3 Feb 2024 20:58:09 GMT
• Title: Data Quality Matters: Suicide Intention Detection on Social Media Posts Using a RoBERTa-CNN Model
• Authors: Emily Lin, Jian Sun, Hsingyu Chen, and Mohammad H. Mahoor
• Abstract summary: We present a novel approach to suicide detection using the cutting-edge RoBERTa-CNN model. RoBERTa-CNN achieves 98% mean accuracy with the standard deviation (STD) of 0.0009. It also reaches over 97.5% mean AUC value with an STD of 0.0013.
• Score: 39.143550443239064
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Suicide remains a global health concern for the field of health, which urgently needs innovative approaches for early detection and intervention. In this paper, we focus on identifying suicidal intentions in SuicideWatch Reddit posts and present a novel approach to suicide detection using the cutting-edge RoBERTa-CNN model, a variant of RoBERTa (Robustly optimized BERT approach). RoBERTa is used for various Natural Language Processing (NLP) tasks, including text classification and sentiment analysis. The effectiveness of the RoBERTa lies in its ability to capture textual information and form semantic relationships within texts. By adding the Convolution Neural Network (CNN) layer to the original model, the RoBERTa enhances its ability to capture important patterns from heavy datasets. To evaluate the RoBERTa-CNN, we experimented on the Suicide and Depression Detection dataset and obtained solid results. For example, RoBERTa-CNN achieves 98% mean accuracy with the standard deviation (STD) of 0.0009. It also reaches over 97.5% mean AUC value with an STD of 0.0013. In the meanwhile, RoBERTa-CNN outperforms competitive methods, demonstrating the robustness and ability to capture nuanced linguistic patterns for suicidal intentions. Therefore, RoBERTa-CNN can detect suicide intention on text data very well.

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