Automatic detection of suicidal ideation on social media using a hybrid CNN–BiLSTM architecture
Keywords:
Natural Language Processing, Suicidal Ideation, BiLSTM, Social Media, Data MiningAbstract
Suicide is a serious public health problem, responsible for more than 700,000 deaths annually worldwide, according to the World Health Organization. Early identification of signs of suicidal ideation can support prevention initiatives and reduce fatal outcomes. This study investigated the application of Natural Language Processing (NLP) and deep learning techniques for the automatic detection of suicidal ideation in social media posts. A hybrid architecture based on Bidirectional Long Short-Term Memory (BiLSTM), combined with convolutional layers and regularization strategies, was proposed. The evaluation was conducted on a Reddit dataset processed in two versions: with and without stopword removal. The results showed that the proposed architecture achieved high performance in both versions, reaching an accuracy of 95.77% and an F1-score of 95.79% on the test set without stopword removal. Despite the higher computational cost, this approach outperformed previous studies reported in the literature. In summary, the proposed model demonstrates potential to support early monitoring strategies for suicidal ideation in digital environments, offering a relevant contribution to public health and suicide prevention.
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References
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