Detecting Suicidality with a Contextual Graph Neural Network (2022)

Abstract: Discovering individuals’ suicidality on social media has become increasingly important. Many researchers have studied to detect suicidality by using a suicide dictionary. However, while prior work focused on matching a word in a post with a suicide dictionary without considering contexts, little attention has been paid to how the word can be associated with the suicide-related context. To address this problem, we propose a suicidality detection model based on a graph neural network to grasp the dynamic semantic information of the suicide vocabulary by learning the relations between a given post and words. The extensive evaluation demonstrates that the proposed model achieves higher performance than the state-of-the-art methods. We believe the proposed model has great utility in identifying the suicidality of individuals and hence preventing individuals from potential suicide risks at an early stage.

## introduction suicide has become a serious problem in society. the oecd (organization for economic cooperation and development) reported that the suicide rate of south korea and the usa was 23.0 and 14.5 deaths per 100,000 population in 2017, which ranked 1st and 8th, respectively 1 . the awareness of the severity of suicide has led researchers to develop suicidality detection models using a deluge of user activity data on social media, which can help capture latent warning signs of suicide in an early stage @xcite @xcite . for example, the prior work showed that linguistic characteristics revealed in social media posts could be linked to suicide risks @xcite ; shing et @xcite . specifically, applying the lexiconbased methods using suicide dictionaries made by domain experts has been reported as effective in capturing linguistic characteristics to detect suicidality. @xcite . while applying the lexicon-based method has been known to be explainable and easy to implement @xcite , it may have a limitation: only focusing on whether each word in a post is matched with the suicide lexicon, not considering the context. for example, as illustrated in figure 1 , there are two sentences: "i cut my wrist" and "i have my hair cut". assuming that the word 'cut' belongs to the suicide dictionary, only the former sentence should be evaluated as having suicidality. however, the latter sentence could also appear to have suicidality if the methods of prior work @xcite are applied. in other words, if the context is incorrectly captured, a model using a suicide lexicon created by experts may not be able to accurately assess the risk of suicidality @xcite . to address this problem, we propose to model the dynamic semantic knowledge between posts and multiple suicide-related words in a suicide dictionary. capturing the posts' document-word association and word co-occurrence is crucial to un-derstanding the contextualized suicidality revealed in social media posts. to this end, we apply a graph neural network to jointly learn word and document embeddings over a contextual graph representing the relations between posts and multiple suicide-related words in the dictionary. we build a heterogeneous network describing the relations (i) between social media posts and multiple words in a suicide dictionary and (ii) between suicide words based on the co-occurrence. as node information in the given graph, a post node includes the contextual representation obtained from pretrained bert @xcite , and a word node contains the suicide risk level information and contextual representation obtained from the fine-tuned word2vec @xcite . we learn the proposed heterogeneous graph using the modified @xcite , contextual graphsage (c-graphsage), to derive a contextualized graph representation. instead of using existing suicide dictionaries, we create a word-level suicide dictionary based on social media data using a computational method (section 3). since the existing suicide-related lexicon mostly consists of clinical terms (e.g., 'suicide by self-administered drug') validated by domain experts @xcite , it may result in a discrepancy with the language used in social media. the created suicide dictionary consists of 279 words and four categories of suicidality levels. we summarize our contributions as follows. ## suicide dictionary a suicide-related word list can help build a simple detector that automatically responds with helpline links to suicidal content. however, the existing english suicide-related lexicon foot_1 mainly was made of clinical terms validated by domain experts @xcite , which results in the discrepancy with the language used in social media. hence, the authors @xcite just used the suicide lexicon as a criterion for checking the presence of a concept in the user's posts. instead of using the existing english suicide lexicon mostly consisting of clinical terms, we propose to create a word-level english suicide dictionary based on social media data. the proposed computational method can be easily applied to other languages that do not have their own suicide lexicons. creating a suicide dictionary. we create a word-level english suicide dictionary in a computational way using the umd reddit suicidality dataset @xcite . the dataset contains 79,569 posts uploaded to 37,083 subreddits of 866 reddit users posted on the r/suicidewatch subreddit from 2008 to 2015. in addition, each post is labeled the suicidality severity conducted by crowdsourcing and domain experts (i.e., no risk, low risk, moderate risk, and severe risk). we only use the posts uploaded to the r/suicidewatch and 15 mental-health-related subreddits (e.g., r/depression, r/anxiety, r/selfharm, etc.) @xcite as a target group and use the posts of users who had not posted on either r/suicidewatch or mental-health related subreddits as a control group. before constructing a dictionary, we anonymize the dataset by removing personally identifiable information such as names, email addresses, and urls. after removing stopwords and lemmatizing the text using spacy @xcite , we extract keywords for each post using keybert @xcite , and then apply the sparse additive generative model (sage) @xcite to determine the words specialized for each label compared to the entire lexicon. finally, the constructed dictionary includes 297 suicide-related words. note that the words belonging to the control group are excluded from the corpus set of each label. validation and correction. we recruited two clinical psychotherapists and a psychiatrist to validate and correct the computationally generated suicide dictionary. all annotators verify how well each label of the suicide word complies with the existing sharing task guideline @xcite , and correct it if it does not meet the criteria. each annotator performs the validation process independently. the final risk label of each suicide word is set to the label agreed by more than or equal to two annotators. as a result of removing 18 differently validated words from all three annotators, there are 279 words in the final dictionary. ## the model we propose a suicidality detection model c-graphsage that can capture the severity of suicidality of a post on social media. figure 2 figure 2 : the overall architecture of the model. we then apply @xcite to the given graph to learn the informative representation of suicide-related context by capturing (i) post-words associations and (ii) relations between suicide-related words. finally, the extracted node presentation from the network is fed into the classification layer. the given post is classified into one of five risk categories: support (su), indicator (in), ideation (id), behavior (br), and attempt (at). ## experiments we evaluate the our proposed model by answering the following research questions: ## results in this section, we present our experiment results to answer the three above research questions. to answer the first question, we evaluate the suicidality detection models (rule-based @xcite and random forest (rf)) with two different suicide dictionaries: (1) the domain knowledgebased one made by experts @xcite , and (2) a computationally created one (ours). as shown in overall, deep learning models with a suicide dictionary (i.e., c-graphsage, 'sdm w/ lexicon', and 'bert w/ lexicon') perform better than the models that use only text information such as c-cnn, sismo, 'sdm w/o lexicon', and 'bert w/o lexicon'. this shows that a model using a suicide dictionary can present the suicide-related context of posts, resulting in high performance. note that 'sdm w/ lexicon' uses the fine-tuned word embedding model to capture domain knowledge from a pre-built suicide dictionary @xcite , whereas 'sdm w/o lexicon' adopts pre-trained fasttext embeddings @xcite for encoding posts. also, 'the bert w/ lexicon' adds the suicide words on the bert-tokenizer. we attribute this to the strength of the graph neural network model that can learn better representations from the relations between posts and words in the suicide dictionary and the associations between suicide words in the suicide-related context. as a result, c-graphsage is helpful in accurately identifying suicidality levels, which shows outstanding utility in preventing suicide risks. ## concluding discussion this paper proposed a suicidality detection model, c-graphsage, which can capture the context of suicidality by learning the relations between social media posts and suicide-related words. using a word-level english suicide dictionary validated by domain experts, the proposed model achieved higher performance than the state-of-the-art methods in detecting suicidality levels. we believe the proposed model has great utility in identifying potential suicidality levels of individuals with social media data, preventing individuals from potential suicide risks at an early stage. ethical concerns. this study is reviewed and approved by the institutional review board (skku2020-10-021). all datasets are anonymized. hence no personal information can be identifiable.