# Detection of Suicide Ideation in Social Media Forums Using Deep Learning
## 2 Background and Related Work  
In recent years, a considerable number of experiments has been developed to emphasize an influencing power of social media on suicide ideation. [PERSON] et al. [7] developed a statistical approach based on a score matching model to derive some distinct markers detecting the transition from a mental health discourse to suicide ideation. According to the authors, this transition can be accompanied by three specific psychological stages: thinking, ambivalence and decision-making. The first stage includes thoughts of anxiety, hopelessness and distress. The second stage is related to lowered self-esteem and reduced social cohesion. The third stage is accompanied by aggression and a suicide commitment plan. Similarly, [PERSON] et al. [14] examined the behavioral shifts of the users who identified a significant growth of tweets with feelings of sadness expressed in the weeks prior to a suicide attempt. Furthermore, a significant increase in tweets with anger emotions were detected the weeks following the suicide attempt. Several studies advocate the impact of social network reciprocal connectivity on users' suicide ideation. [PERSON] [15] observed the users' behavior changes in reaction to a suicide case which happened within the social media group. [PERSON] et al. [16] emphatically highlighted the geographic correlation between the suicide mortality rates and the occurrence of risk factors in tweets. [PERSON] et al. [17] studied the tweets containing suicide ideation based on the users' behavior in social network interactions resulting in a high degree of reciprocal connectivity and strengthening the bonds between the users. Another interesting observation is the impact of celebrity suicides on suicide ideation development among the members of online communities. [PERSON] et al. [9] examined the attributes of suicidal interests of Reddit users related to the copycat or [PERSON] effect [18]. His work indicates a notable increase of users' posting frequency and the shifts in their linguistic behavior after the reports of celebrity suicides. This shift was observed in a direction towards more negative and self-focused posts with lower social integration. Similarly, [PERSON] et al. [19] conducted profound research on one million Twitter posts following the suicide of 26 prominent celebrities in Japan between the years 2010 and 2014. Identification of regular language patterns in social media text leads to a more effective recognition of suicidal tendencies. It is often supported by applying various machine learning approaches on different NLP techniques. [PERSON] et al. [20] built a suicide note analysis method to detect suicide ideation using binary Support Vector Machine (SVM) classifiers. [PERSON] et al. [21] created a psychological lexicon based on a Chinese sentiment dictionary (Hownet). He applied the SVM approach to identify a classification for developing a real-time suicide ideation detection system deployed in Chinese Weibo. [PERSON] et al. [22] demonstrated that machine learning algorithms are efficient in differentiating people to those who are and who are not at suicidal risk. [PERSON] et al. [23] studied a suicidal intent of Japanese Twitter users in their 20s, where he stated that a language framing is important for identifying suicidal markers in the text. For instance, \"want to suicide\" expression is more frequently associated with a lifetime suicidal intent than \"want to die\" expression. [PERSON] et al. [24] proved that it is possible to distinguish the level of concern among suicide-relatedposts using both human codes and an automatic machine learning classifiers (LR, SVM) on TF-IDF features. [PERSON] et al. [25] identified 125 Twitter users and followed their tweets preceding the data available prior to their suicide attempt. Using simple and linear classifiers, they found 70% of the users with a suicide attempt and identified their gender with 91.9% accuracy. [PERSON] et al.