Comparative Analysis of SVM and LSTM for Detecting Early Suicide Ideation on Social Media

Every year, more than 800,000 people die by suicide, making it a global mental health concern (WHO). In this digital era, social media provides a forum for people to share mental anguish such as suicidal thoughts. In this study, suicidal ideation in similar text data found from Twitter was compared to see the effectiveness of Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) models. The efficiency of each model was assessed using a dataset taken from the r/SuicideWatch subreddit and modified to resemble the tweet format. SVM uses TF-IDF and SVD, while LSTM uses GloVe embeddings. Both models have the potential to detect suicidal content based on the results, with SVM achieving a high accuracy of 91% and LSTM slightly higher at 92%. To identify suicide risk early, based on the study, both models performed well in using social media analytics to support prevention initiatives. For future research, it is recommended to collect more tested data and optimize hyperparameters. Model interpretation can be enhanced by incorporating psychological knowledge, especially from scientific or clinical psychology to further aid mental health efforts.