Automatic extraction of informal topics from online suicidal ideation

Abstract

BackgroundSuicide is an alarming public health problem accounting for a considerable number of deaths each year worldwide. Many more individuals contemplate suicide. Understanding the attributes, characteristics, and exposures correlated with suicide remains an urgent and significant problem. As social networking sites have become more common, users have adopted these sites to talk about intensely personal topics, among them their thoughts about suicide. Such data has previously been evaluated by analyzing the language features of social media posts and using factors derived by domain experts to identify at-risk users.ResultsIn this work, we automatically extract informal latent recurring topics of suicidal ideation found in social media posts. Our evaluation demonstrates that we are able to automatically reproduce many of the expertly determined risk factors for suicide. Moreover, we identify many informal latent topics related to suicide ideation such as concerns over health, work, self-image, and financial issues.ConclusionsThese informal topics topics can be more specific or more general. Some of our topics express meaningful ideas not contained in the risk factors and some risk factors do not have complimentary latent topics. In short, our analysis of the latent topics extracted from social media containing suicidal ideations suggests that users of these systems express ideas that are complementary to the topics defined by experts but differ in their scope, focus, and precision of language.

Suicide Risk Assessment Using Machine Learning and Social Networks: a Scoping Review

“…From the full-text review, 16 articles were then selected for inclusion [26,[42][43][44][45][46][47][48][49][50][51][52][53][54][55][56]. The flow diagram representing the search process is shown in Fig.…”

“…Grant et al 2018 [49] automatically extracted informal latent recurring topics of suicidal ideation found in social media posts using Word2vec. The proposed method uses descriptive analysis and can identify similar issues to the expert's risk factors.…”

“…Feature extraction and feature selection were identified as main objectives in four different studies (4/16, 25%) [48,50,53,56]. The remaining categories (Entity Recognition, Theme Identification and Emotion Recognition) were identified only in a study (1/16, 6.25%) [47,49,55]. In total, 4 of 16 studies (25%) can be grouped in two categories, involving text classification (3/4) [47,48,53] or score estimation (1/4) [56].…”

“…Other GSNs used were Weibo (2/16, 12.5%), Facebook, Instagram, Tumblr, and Reddit (1/16, 6.25%). Three studies used OHCs (18.75%), two of them used suicide-related subreddit [42,49], and the other one used a Sina microblog [51].…”

“…2 The distribution of the included studies according to the year of publication studies recruited 697 participants and then collected data from their Weibo accounts [56]. The other two studies analyzed the user's data collected to report the number of unique users involved in the study (N = 3873; N = 63,252) [48,49]. Although using basic statistics to describe dataset is defined as a relevant factor regarding the reliability of MLbased studies in the health domain, as suggested by Luo et al's guidelines [37].…”