Creating a Chinese suicide dictionary for identifying suicide risk on social media

Abstract

Introduction. Suicide has become a serious worldwide epidemic. Early detection of individual suicide risk in population is important for reducing suicide rates. Traditional methods are ineffective in identifying suicide risk in time, suggesting a need for novel techniques. This paper proposes to detect suicide risk on social media using a Chinese suicide dictionary.Methods. To build the Chinese suicide dictionary, eight researchers were recruited to select initial words from 4,653 posts published on Sina Weibo (the largest social media service provider in China) and two Chinese sentiment dictionaries (HowNet and NTUSD). Then, another three researchers were recruited to filter out irrelevant words. Finally, remaining words were further expanded using a corpus-based method. After building the Chinese suicide dictionary, we tested its performance in identifying suicide risk on Weibo. First, we made a comparison of the performance in both detecting suicidal expression in Weibo posts and evaluating individual levels of suicide risk between the dictionary-based identifications and the expert ratings. Second, to differentiate between individuals with high and non-high scores on self-rating measure of suicide risk (Suicidal Possibility Scale, SPS), we built Support Vector Machines (SVM) models on the Chinese suicide dictionary and the Simplified Chinese Linguistic Inquiry and Word Count (SCLIWC) program, respectively. After that, we made a comparison of the classification performance between two types of SVM models.Results and Discussion. Dictionary-based identifications were significantly correlated with expert ratings in terms of both detecting suicidal expression (r = 0.507) and evaluating individual suicide risk (r = 0.455). For the differentiation between individuals with high and non-high scores on SPS, the Chinese suicide dictionary (t1: F1 = 0.48; t2: F1 = 0.56) produced a more accurate identification than SCLIWC (t1: F1 = 0.41; t2: F1 = 0.48) on different observation windows.Conclusions. This paper confirms that, using social media, it is possible to implement real-time monitoring individual suicide risk in population. Results of this study may be useful to improve Chinese suicide prevention programs and may be insightful for other countries.

Researching Mental Health Disorders in the Era of Social Media: Systematic Review

“…The instruments used to detect suicidal ideation and the possibility of an individual committing suicide were the Suicide Probability Scale [32,39,48], the Acquired Capability for Suicide Scale [30], and the Interpersonal Needs Questionnaire [30]. Satisfaction with life and well-being were measured with the Satisfaction with Life Scale [28,34], the Positive and Negative Affect Schedule [55], and the Psychological Well-Being Scale [55].…”

“…It is employed to classify the polarity of a given text into categories such as positive, negative, and neutral [77]. Several studies [24,28,30,32,34,39, 49,50,52-55,57,60,65-68,70] used the well-known linguistic inquiry and word count (LIWC) [78] to extract potential signals of mental problems from textual content (eg, the word frequency of the first personal pronoun “I” or “me” or of the second personal pronoun, positive and negative emotions being used by a user or in a post). OpinionFinder [79] was used by Bollen et al [71] and SentiStrength [80] was used by Kang et al [27] and by Durahim and Coşkun [47] to carry out sentiment analysis.…”

Extracting psychiatric stressors for suicide from social media using deep learning

“…Most previous studies focused on analyzing the association between suicidal ideation and the linguistic features of the contents (i.e. lexical analysis [ 14 – 18 ]) or posting behaviors (i.e. posting frequency [ 5 , 19 ]) on social media platforms.…”

Dynamics of an idiostyle of a Russian suicidal blogger

“…The results of our study that are certainly preliminary have proved that it is viable to use software, particularly LIWC with a Russian dictionaries, for processing a large massive of texts in order to identify stable and varying characteristics of idiostyle with respect to topic dimension. However, it will be necessary to verify and expand internal Russian dictionary and to create special dictionaries for suicide-related studies as it was done for Chinese (Lv et al, 2015). In addition, we are planning to extend the list of linguistic parameters and add linguistic complexity, syntactic parameters, etc.…”