Learning Models for Suicide Prediction from Social Media Posts (2021)

Abstract: We propose a deep learning architecture and test three other machine learning models to automatically detect individuals that will attempt suicide within (1) 30 days and (2) six months, using their social media post data provided in the CL-Psych-Challenge. Additionally, we create and extract three sets of handcrafted features for suicide detection based on the three-stage theory of suicide and prior work on emotions and the use of pronouns among persons exhibiting suicidal ideations. Extensive experimentations show that some of the traditional machine learning methods outperform the baseline with an F1 score of 0.741 and F2 score of 0.833 on subtask 1 (prediction of a suicide attempt 30 days prior). However, the proposed deep learning method outperforms the baseline with F1 score of 0.737 and F2 score of 0.843 on subtask2 (prediction of suicide 6 months prior).

## introduction according to world health organization (who) 1 , close to 800,000 people die due to suicide every year, which is one person every 40 seconds. the us centers for disease control and prevention (cdc) 2 claimed that suicide was the tenth leading cause of death overall in the united states. recently, there has been a trend in using natural language processing (nlp) techniques on unstructured physician notes from electronic health record (ehr) data to detect high-risk patients @xcite . with the proliferation of social media where there is free sharing of information, mining data from these platforms has become a natural way to extend the above body of work in more natural settings. consequently, researchers have started to apply machine learning and nlp based techniques to detect suicide ideation on social media platforms @xcite . some of them focused on handcrafted features, including tf-idf @xcite , liwc @xcite , n-gram, part-of-speech (pos) and emotions @xcite @xcite , while others explored language embeddings @xcite @xcite . in this paper, we present several approaches to detect suicide ideation from twitter posts (1) 30 days before the attempt and (2) six months before the attempt. we use the dataset provided by the clpsych 2021 shared tasks @xcite towards this goal. the main contributions of our work are: ## method before we describe the methods in detail we provide a summary of the features used in our work. we use two classes of features: latent features and handcrafted features. these are described in the sections below. ## results ## analysis/discussion the results reported in as a result, in the following experiments, we randomly split the training set into 80% for training and 20% for validation, and use the models that performed best on the validation set to predict suicide in the test set. the new performance results on the test set are shown in we noted that in subtask 1, knn and svm(eb) performed best in terms of f1, f2 and trp. the best auc was achieved by knn only, and the best fpr was achieved by rf. in subtask 2, c-att performed best in terms of f1, f2 and trp; the best fpr was achieved by rf; and the best auc was achieved by baseline. our experiment results would indicate that: ## conclusion in this work, we introduce c-attention model and test other machine learning models to automatically detect suicidal individuals based on the latent feature (doc2vec) and handcrafted features including emotions, pos, and three-step theory of suicide and suicide dictionary. our results show that both knn and svm(eb) achieved the best f1 score of 0.741 and f2 score of 0.833 on subtask 1 (prediction of a suicide attempt 30 days prior), and c-att reached the best f1 score of 0.737 and f2 score of 0.843 on subtask 2 (prediction of suicide 6 months prior). ultimately, this work supports the use of social media as an avenue to better predict and understand the experience of suicidal thoughts. however more