Abstract

BackgroundSuicide has been one of the leading causes of deaths in the United States. One major cause of suicide is psychiatric stressors. The detection of psychiatric stressors in an at risk population will facilitate the early prevention of suicidal behaviors and suicide. In recent years, the widespread popularity and real-time information sharing flow of social media allow potential early intervention in a large-scale population. However, few automated approaches have been proposed to extract psychiatric stressors from Twitter. The goal of this study was to investigate techniques for recognizing suicide related psychiatric stressors from Twitter using deep learning based methods and transfer learning strategy which leverages an existing annotation dataset from clinical text.MethodsFirst, a dataset of suicide-related tweets was collected from Twitter streaming data with a multiple-step pipeline including keyword-based retrieving, filtering and further refining using an automated binary classifier. Specifically, a convolutional neural networks (CNN) based algorithm was used to build the binary classifier. Next, psychiatric stressors were annotated in the suicide-related tweets. The stressor recognition problem is conceptualized as a typical named entity recognition (NER) task and tackled using recurrent neural networks (RNN) based methods. Moreover, to reduce the annotation cost and improve the performance, transfer learning strategy was adopted by leveraging existing annotation from clinical text.Results & conclusionsTo our best knowledge, this is the first effort to extract psychiatric stressors from Twitter data using deep learning based approaches. Comparison to traditional machine learning algorithms shows the superiority of deep learning based approaches. CNN is leading the performance at identifying suicide-related tweets with a precision of 78% and an F-1 measure of 83%, outperforming Support Vector Machine (SVM), Extra Trees (ET), etc. RNN based psychiatric stressors recognition obtains the best F-1 measure of 53.25% by exact match and 67.94% by inexact match, outperforming Conditional Random Fields (CRF). Moreover, transfer learning from clinical notes for the Twitter corpus outperforms the training with Twitter corpus only with an F-1 measure of 54.9% by exact match. The results indicate the advantages of deep learning based methods for the automated stressors recognition from social media.

Highlights

  • Suicide has been one of the leading causes of deaths in the United States

  • In order to confirm the superiority of the convolutional neural networks (CNN) model, we evaluated several traditional machine learning algorithms, including Extra Trees (ET), Random Forest (RF), Logistics Regression (LR) and Support Vector Machine (SVM) with Radial Basis Function (RBF) as the kernel, as well as a Bidirectional Long Short-Term Memory (Bi-Long short-term memory (LSTM)) classifier

  • Suicide-related tweet classification Table 4 lists the performance of suicide-related tweets classification, using the CNN based algorithm and GolVe Twitter embedding features of different dimensions

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Summary

Introduction

One major cause of suicide is psychiatric stressors. The detection of psychiatric stressors in an at risk population will facilitate the early prevention of suicidal behaviors and suicide. The national cost of suicides and suicidal behavior in the United States was $93.5 billion in 2013 after adjustment for under-reporting [3]. Suicide and suicidal behaviors cause unbearable impacts on the specific individuals and surviving family and friends, and create long lasting effects on whole communities [5]. As one of the major causes to suicide, psychiatric stressors are psychosocial or environmental factors that can profoundly impact cognition, emotion, and behavior of people [8]. The identification of psychiatric stressors is critical to understanding the causes of potential suicide and suicidal behaviors for a specific individual, which is critical to provide a tailored and precise intervention strategy. If the primary stressor for an individual is identified as school bullying, we can offer intensive individual interventions that provide the victim with individual support through meetings with students and parents, counseling, and sustained child and family support [9]

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