Maryann Gold Azodo

In today’s digital age, threats to life have become alarmingly common, infiltrating everything from social media platforms to personal communications. About 11.3% of people experienced suicidal ideation over the past year, 5.5% of adults in the United States reported serious suicidal thoughts in the past year, and 10-14% of children and adolescents experienced cyberbullying globally. This study developed machine learning models to detect life-threatening statements across different model categories by employing hyperparameter tuning and Adam optimization. Six models were built using RF, SVM, FFNN, LSTM, pre-trained BERT, and T5, and evaluated using benefit and cost measures and confusion matrices. The outcome indicates that the six models performed well, but the FFNN model outperformed all other models by achieving an accuracy of 98.86%, precision of 98.86%, MCC of 97.72%, Recall of 98.86%, MAE of 0.0113, and RMSE of 0.1067. It also achieved the highest number of accurately classified instances and the lowest number of misclassified instances, which demonstrates that the FFNN model exhibits robustness and precision, making it highly reliable for detecting life-threatening statements compared to other models. Having achieved a very high accuracy, the method still lacks an in-depth understanding of contextual information. Therefore, further studies may focus on incorporating contextual information for more accurate classification of life-threatening statements and integrating the developed model into a real-time detection system.