# Detection of Suicide Ideation in Social Media Forums Using Deep Learning
## 1 Introduction  
Every year, almost 800,000 people commit suicide. Suicide remains the second leading cause of death among a young generation with an overall suicide rate of 10.5 per 100,000 people. It is predicted that by 2020, the death rate will increase to one every 20 s [1]. Almost 79% of the suicides occur in low- and middle-income countries where the resources for the identification and management is often scarce and insufficient. Suicide ideation is viewed as a tendency to end ones' life ranging from depression, through a plan for a suicide attempt, to an intense preoccupation with self-destruction [2]. At-risk individuals can be recognized as suicide ideators (or planners) and suicide attempters (or completers) [3]. The relationship between these two categories is often a subject of discussion in research communities. According to some studies, most of the individuals with suicide ideation do not make suicide attempts. For instance, [PERSON] et al. [4] believes that most of the oft-cited risk factors (depression, hopelessness, frustration) connected with suicide are the predictors of suicide ideation, not the progression from the ideation to attempt. However, [PERSON] et al. [5] reveals that a suicide ideator and suicide attempter can be quite similar to \"several variables assumed to be risk factors for suicidal behavior\". In WHO countries, early detection of suicide ideation has been developed and implemented as a national suicide prevention strategy to work towards the global market with the common aim to reduce the suicide rates by 10% by 2020 [1]. Over recent years, social media has become a powerful \"window\" into the mental health and well-being of its users, mostly young individuals. It offers anonymous participation in different cyber communities to provide a space for a public discussion about socially stigmatized topics. Generally,more than 20% of suicide attempters and 50% of suicide completers leave suicide notes [6]. Thus, any written suicidal sign is viewed as a worrying sign, and an individual should be questioned on the existence of individual thoughts. According to [PERSON] et al. [7], social media text, such as blog posts, forum messages, tweets, and other online notes, is usually recorded in the present and is well preserved. In comparison to an offline text, it can minimize any misleading text interpretations produced by a retrospective analysis. Social media with its mental health-related forums has become an emerging study area in computational linguistics. It provides a valuable research platform for the development of new technological approaches and improvements which can bring a novelty in suicide detection and further suicide risk prevention [8]. It can serve as a good intervention point. [PERSON] et al. [9] studied the posting activities of Reddit SuicideWatch users who follow news about celebrity suicides. He introduced a method that can be efficient in preventing high profile suicides. [PERSON] et al. [7] studied the shift from a mental health discourse to suicide ideation in Reddit social media. He developed a propensity score matching-based statistical approach to derive the distinct markers of this shift. Recently, [PERSON] et al. [10] has developed a novel data protecting the solution and advanced optimization strategy (AvgDiffLDP) for early detection of suicide ideation. Apart from traditional text classification approaches, deep learning methods have already made an impressive advance in the field of computer vision and pattern recognition. While traditional machine learning approaches liaise heavily on time-consuming and often incomplete handcrafted features, neural networks based on dense vector representations can produce superior results on various Natural language processing (NLP) tasks [11]. The growing success of word embedding [12; 13] and deep neural networks are reflected in outperforming more traditional machine learning systems for suicide risk assessments.