Attentive multi-channel convolutional neural network for suicide ideation detection

Abstract

Suicide is one of the leading causes of death worldwide and it is in most cases a result of significant depression experienced by people. Depression itself can stem from various sources such as personal financial instability or poverty that deprives people of the ability to acquire various basic/essential needs, betrayal by close friends, divorce, social media backlash, to name but a few. Early detection of suicidal intent among people can help inform interventions to control suicidal risks such as counselling with the help of recent advancements in the performance of deep learning algorithms and techniques for sequence modelling from RNNs to LSTM models and to transformer models. Thanks to the breakthrough attention mechanism technique, transformer models have proven to perform better on natural language processing tasks and modelling of long sequences than the previous solutions based on RNNs and LSTMs. This research focuses on using social media data from the Twitter and Reddit social media platforms to detect suicide ideation using a multi-channel ID convolutional neural network with attention. Our proposed architecture further uses glove vectors for embedding layer initialization in the model. The overall suicide ideation detection class if formulated as a binary classification task with each text entry falling under either a suicidal(1) or non-suicidal(0) class. Because labeled text data relating to our reasearch topic was extremely hard to get, we scrape data from the r/suicide and r/depression sub-reddits and label this as suicidal given that suicide in many cases progresses from a person being mentally depressed due to persistent difficulties they are faced with at given points in their life and they seem to see/find no solution to. Text from non suicide or depression related topics are scraped from twitter to provide features for the non-suicidal class label.