Cross-Lingual Suicidal-Oriented Word Embedding toward Suicide Prevention (2020)

Abstract: Early intervention for suicide risks with social media data has increasingly received great attention. Using a suicide dictionary created by mental health experts is one of the effective ways to detect suicidal ideation. However, little attention has been paid to validate whether and how the existing dictionaries for other languages (i.e., English and Chinese) can be used for predicting suicidal ideation for a low-resource language (i.e., Korean) where a knowledge-based suicide dictionary has not yet been developed. To this end, we propose a cross-lingual suicidal ideation detection model that can identify whether a given social media post includes suicidal ideation or not. To utilize the existing suicide dictionaries developed for other languages (i.e., English and Chinese) in word embedding, our model translates a post written in the target language (i.e., Korean) into English and Chinese, and then uses the separate suicidal-oriented word embeddings developed for English and Chinese, respectively. By applying an ensemble approach for different languages, the model achieves high accuracy, over 87%. We believe our model is useful in accessing suicidal ideation using social media data for preventing potential suicide risk in an early stage.

## introduction as online social media has become the norm to share our daily lives, people often share their emotions, feelings, and mental state. this has spurred scholars to identify diverse mental health problems such as depression, anxiety, bipolar disorder, or suicidal thoughts using plenty of user behavior data on online social media @xcite @xcite . such user behavior data can provide a cue for identifying individual mental state or even suicide risk @xcite @xcite , which can be used to support mental health care @xcite . among the diverse mental health problems, suicide has become one of the big and emerging concerns worldwide. the oecd (organization for economic cooperation and development) reported 11.2 deaths per 100,000 population in oecd countries in 2017 (oecd, 2020). in particular, the suicide rate of korea and the usa was 24.6 and 13.9 deaths per 100,000 population in 2016, which ranked 1st and 8th, respectively. the awareness of the severity of suicide has led researchers to assess mental health using social media data for recognizing potential warning signs of suicide in an early stage @xcite . in particular, linguistic characteristics (e.g., frequently used words like 'family', 'sad', or 'dream') of social media posts have been extensively investigated @xcite . as prior research showed that certain linguistic features revealed in an individual language could be linked to suicide risk @xcite , there have been attempts to develop machine-learning models using a suicide dictionary, which was created and curated by mental health experts. for example, an english suicide dictionary was created and validated by four clinical psychiatrists @xcite ; a chinese suicide dictionary was curated by eleven mental health experts @xcite . the predictive power of such suicide dictionaries with domain knowledge (in english or chinese) in identifying suicide risk from an english-or chinese-written social media post has been demonstrated @xcite . however, little attention has been paid to validate whether the existing dictionary developed for the specific language (e.g., english or chinese) can be used for predicting suicidal ideation with other languages (e.g., korean or japanese), where any suicide dictionary has not yet been developed. it is essential to investigate whether and how existing suicide dictionaries developed by domain experts can be utilized by predicting suicidal ideation for non-english or non-chinese spoken countries because building and validating such a knowledge-based dictionary requires much effort. to shed light on this issue, we propose a crosslingual suicidal ideation detection model that can identify whether a given social media post includes suicidal ideation or not. to utilize the existing suicide dictionaries developed for other languages (i.e., english and chinese) in word embedding, our model translates a post written in the target language (i.e., korean) into english and chinese and then uses the separate word embeddings developed for english and chinese, respectively. our model then uses attention to make a representation for post embedding. the attention helps find words that are more relevant to suicidal ideation, thereby obtaining a better post representation. by applying an ensemble approach for different languages, which can reflect linguistic or cultural differences @xcite , our proposed model finally predicts suicidal ideation of the given post in korean. we highlight the main contributions of our work as follows. note that the korean suicidal-oriented word-embedding is built by a computational approach without medical knowledge base but shows a considerable performance in suicidal ideation detection. we believe the suicidal-oriented word-embeddings can be useful for researchers who want to access suicidal ideation using social media data for preventing potential suicide risk at an early stage. ## cross-lingual suicidal ideation detection model we propose a suicidal ideation detection model that can identify whether a given post includes suicidal ideation or not. to utilize the existing suiciderelated dictionaries developed for other languages (i.e., english and chinese) in word embedding, our model translates a post written in the target language (i.e., korean) into english and chinese and then uses the separate word embeddings developed for english and chinese, respectively. note that we use naver papago @xcite for translation, which is known to be an efficient translator from korean to other languages. by applying an ensemble approach for different languages, our proposed model finally predicts suicidal ideation of the given post in korean. figure 1 illustrates the overall architecture of our proposed model. ## suicide data to develop models for predicting suicidal ideation for a post written in korean, we collected the suicide-related and non-suicide-related korean posts from naver cafe 1 . to improve the model performance, we further collected data for generating suicide word embeddings for chinese, english, and korean, respectively. note that all the collected data is anonymized, hence no user information can be identifiable. ## experiments we evaluate the proposed cross-lingual suicidal ideation detection model by answering the following research questions: ## conclusion this paper proposed a cross-lingual suicidal ideation detection model that provides a costeffective way to predict suicidal ideation with social media data written in a language where no suicide dictionary exists. we proposed to apply (i) suicidal-oriented word embeddings developed for other languages (i.e., english and chinese), (ii) attention mechanism for post representation, and (iii) an ensemble approach to reflect potential cultural and language difference. the proposed model achieved high accuracy, over 87%, signifying its great utility in detecting suicidal ideation using social media data for preventing potential suicide risk in an early stage.