# Detecting Suicidal Ideations in Online Forums with Textual and Psycholinguistic Features
###### Abstract  
Suicide is a global public health problem that takes hundreds of thousands of lives each year. The key to effective suicide prevention is early detection of suicidal ideations and timely intervention. However, several factors hinder traditional suicide risk screening methods. Primarily, the social stigma associated with suicide presents a challenge to suicidal ideation detection, as existing methods require patients to explicitly communicate their suicidal propensities. In contrast, progressively more at-risk people choose online platforms--such as Reddit--as their preferred avenues for sharing their suicidal experiences and seeking emotional support. As a result, these online platforms have become an unobtrusive source of user-generated textual data that can be used to detect suicidality with supervised machine learning and natural language processing techniques. In this paper, we proposed a suicidal ideation detection approach that combines textual and psycholinguistic features extracted from the Reddit forum. Subsequently, we selected the most informative features using the Boruta algorithm and employed four classifiers: logistic regression, naive Bayes, support vector machines, and random forest. The naive Bayes models trained with the combination of term frequency-inverse document frequency (TF-IDF) and National Research Council (NRC) features demonstrated the highest performance, obtaining a F1 score of 70.99%. Our experimental results illustrate that a combination of textual and psycholinguistic features yields better classification performance compared to using those features separately. suicidal ideation detection; machine learning; natural language processing; Reddit; psycholinguistic features +
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generally characterized as passive and active. Examples of passive suicidal ideations may include fleeting thoughts about committing suicide, imagining one's death, or wishing to be dead. In comparison, active suicidal ideation involves deliberately planning and preparing for an attempt, for example, by obtaining a means of suicide such as firearms or drugs. Even though passive suicidal ideations are believed to present lower risk [7], both types ought to be thoroughly assessed by clinicians, because whether active or passive, the underlying motivation is the same--terminating one's own life. Early identification of suicidal ideations is one of the four critical strategies for effective prevention, as outlined in the WHO guidelines [8]. Other strategies also include limiting access to methods of suicide, responsible reporting of suicide cases by the media, and supporting adolescents in cultivating socio-emotional skills. By detecting suicidal ideation in its early phases, healthcare professionals can promptly intervene and provide the necessary care [9]. However, several factors hinder suicidal ideation detection, including limited access to mental health services, scarcity of trained clinicians, and fear of stigmatization [10]. The stigmatization faced by suicidal individuals prevents them from accessing social support [8] and may impede the current detection methods, such as interviews and questionnaires, as they entail explicit disclosure of suicidal propensities [11]. In recent years, progressively more at-risk people have chosen social media and online forums as their preferred avenues for sharing suicidal thoughts and seeking support, largely due to the anonymity online platforms provide [12]. As a result, there is a significant body of work that addresses suicidal ideation detection using user-generated content from online platforms. Social media sites such as Twitter, Facebook, Reddit, and Weibo have been commonly used as sources of data. Out of these, Reddit has appeared as the most notable one owing to its characteristic attributes, such as a high character limit on posts, emphasis on anonymity, and prevalence of moderated online subcommunities dedicated to various mental health topics [13].