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We propose a deep learning architecture and test three other machine learningmodels to automatically detect individuals that will attempt suicide within (1)30 days and (2) six months, using their social media post data provided in theCLPsych 2021 shared task. Additionally, we create and extract three sets ofhandcrafted features for suicide risk detection based on the three-stage theoryof suicide and prior work on emotions and the use of pronouns among personsexhibiting suicidal ideations. Extensive experimentations show that some of thetraditional machine learning methods outperform the baseline with an F1 scoreof 0.741 and F2 score of 0.833 on subtask 1 (prediction of a suicide attempt 30days prior). However, the proposed deep learning method outperforms thebaseline with F1 score of 0.737 and F2 score of 0.843 on subtask 2 (predictionof suicide 6 months prior).

Learning Models for Suicide Prediction from Social Media Posts