Detecting Suicide Risk in Online Counseling Services: A Study in a Low-Resource Language (2022)

Abstract: With the increased awareness of situations of mental crisis and their societal impact, online services providing emergency support are becoming commonplace in many countries. Computational models, trained on discussions between help-seekers and providers, can support suicide prevention by identifying at-risk individuals. However, the lack of domain-specific models, especially in low-resource languages, poses a significant challenge for the automatic detection of suicide risk. We propose a model that combines pre-trained language models (PLM) with a fixed set of manually crafted (and clinically approved) set of suicidal cues, followed by a two-stage fine-tuning process. Our model achieves 0.91 ROC-AUC and an F2-score of 0.55, significantly outperforming an array of strong baselines even early on in the conversation, which is critical for real-time detection in the field. Moreover, the model performs well across genders and age groups.

## introduction the world health organization (who) lists suicide as one of the most salient causes of death world-wide, causing more deaths than breast cancer or war @xcite . with close to 1 million lives taken directly by suicide every year, and over 25 million suicide attempts, suicide incurs a lasting impact on families and communities. identifying individuals at risk ahead of time and providing them with psychological and medical support is a key step in suicide prevention @xcite . in this paper, we tackle the task of detecting suicide ideation (si). specifically, we aim at the detection of si of individuals contacting online counseling hot-lines in low-resource and morphologicallyrich languages. we focus on anonymous data from online, text-based (chat), support services. such services are available in many countries, allowing for confidential and immediate help to those in distress, and play a critical role in suicide prevention @xcite @xcite . empirical evidence suggests that at-risk individuals seek help in close proximity to actual suicide attempts @xcite , thus it is critical to identify suicide risks as early as possible during the session. there is a growing body of work on assessing suicide risk from english texts (whether in social media posts or from counseling sessions), but there is an acute lack of nlp resources that could be used for detection of suicide risk in other languages @xcite . we directly address this gap by focusing on suicide risk detection from online counseling services in hebrew, which is a low-resource and morphologically-rich language that challenges traditional nlp tasks @xcite . our proposed approach for the si detection task, called si-bert, extends a generic pretrained model with a small set of out of vocabulary (oov) tokens, pretraining the language model on a masked lm task and fine tuning for the si classification task. we further train a logistic regression model, based on a manually crafted lexicon of suicide ideation terms (vetted by domain experts). we then create an ensemble model by training si-bert together with the lexicon. both si-bert and the ensemble model outperform alternative approaches ranging from w2v (word2vec) embeddings and feed-forward networks to hebrew psychological lexicon. additionally, the ensemble model achieves 82% roc-auc even when processing only the first few utterances (20%) of the help-seeker, suggesting it can be used to enhance early detection of suicide risk when deployed in the field. we analyze the approach with respect to different demographics, speaker focus and text truncation, and provide a few examples, qualitatively illustrating the benefits of our model. our work goes the first step in helping counselors identify and treat at-risk individuals in real time. ## the dataset sahar ( https://sahar.org.il ), hebrew acronym for "aid and attention online", is the leading chatline in israel, focusing on suicide prevention, and emotional distress relief. relieving the emotional distress of help-seekers is a crucial step in the process of suicide prevention @xcite . the organization handles more than 10, 000 chat sessions a year, and these numbers have increased significantly during the covid-19 pandemic @xcite . sahar's counselors are volunteers over 24 years old that completed a special training program by licensed clinicians. they are trained to use a special support language that is based on the conversation dynamics. during shifts, there are also therapists on duty who monitor the conversations and provide professional support if needed to the counselors. at the end of each session, the counselor is asked to summarize the conversation and to complete a short survey. a conversation is defined as exhibiting si if the counselor answered "yes" to the question "did the subject of suicide come up in the conversation?" the sahar corpus the sahar corpus contains 44,506 chat sessions which took place in the span of five years (2017-january 2022). of these, 17,564 are labeled with a true/false to designate whether the chat exhibited si. seventeen percent (3097/17564) of the labeled sessions are flagged with a positive si label. for the remainder of this paper, the term sahar dataset will refer to the 17,564 labeled sessions in the corpus. a session includes the utterances generated by the help-seekers and the counselor, delimited by a separating token. beyond the si label, counselors are requested to select the prominent topics in each conversation (one to three topics chosen from a predefined list). figure 1 shows the discussed topics distribution in the data. as shown in the figure, loneliness and depression are the most common topics discussed in sahar platform and a large share of those conversation exhibits positive si. these findings coincide with psychology literature which depicts depression (gijzen et al., 2021; moitra et al., 2021) and loneliness (mcclelland et al., 2020) as powerful predictors for suicide ideation. an interesting observation from figure 1 is, that si is prevalent across all of the reported topics, and is not exclusive to a certain topic. unfortunately, we are not able to share the sahar corpus due to its sensitive and private content. nor are we able to share the trained language model, since it was shown language models can be manipulated to reveal training data (e.g @xcite ). we do provide however a repository with the experiments' code and lexicons used in this paper to support transparency and reproducibility. ## computational approach our approach is based on an ensemble method that extends a generic plm to the si domain, and leverages it with a fixed set of manually crafted (and clinically approved) set of suicidal cues. we expand on each component of the ensemble model in turn. ## evaluation we compared the ensemble model to the baseline models described below. the input to all models is a pre-processed chat that concatenates the utterances of the help-seeker and removes non-hebrew characters and urls. (see subsection 6.4 for a comparison with the case of also including the utterances of the counselor). for each bag of words based model (w2v, tf-idf, hpl), the embeddings were scaled to [0,1] range and fed into a logistic regression model. ## results in this section we report and discuss results from four perspectives: (i) entire sessions results, (ii) early detection, (iii) results on different demographic groups, and (iv) using the turn-taking structure vs. focusing on the utterances of the helpseeker. all results are reported using 5-fold cross validation. we keep the label imbalance unchanged in order to increase the potential use of the models for real time si detection, discussing the false positive-false negative trade-off. the input to all models used in subsection 6.1 -subsection 6.3 consists of the concatenated utterances of the helpseeker in each session, while in subsection 6.4 we also provide results for a turn-taking scenario. ## discussion the results in the previous section demonstrate the need for adequate adaptation of plms into a specific domain with obvious challenges in processing low-resource languages. domain-tailored lexicons serve as strong baselines, with high precision and competitive f1 and f2 scores but they fall short in terms of recall and auc. as plms tend to capture more nuanced expressions of si -combining both approaches and careful fine-tuning improved the ability to detect si early on in the chat. we conclude this work discussing three illustrative examples (see in utterance i ( while it does not match any of the lexical items in the predefined lexicons, the second utterance ( the third utterance is not considered by experts to exhibit si. indeed, si-bert (and the ensemble) correctly classifies it as a negative example. on the other hand, ft-bert assigns it a positive label. we hypothesize that the combination of "bad thoughts" and "(a couple of) sleeping pills" triggered the naive ft-bert, while si-bert better captures the nuanced context. this example further demonstrates the benefits of fine-tuning the vanilla alephbert not only for the classification task but also fine-tuning the language model on the domain-specific data through a masked lm task. we end this section briefly mentioning some limitations of the approach. first, our approach does not explicitly account for the discourse structure of the sessions. it may be that encoding the full conversational context may improve performance. second, the ensemble approach relies on a handcrafted lexicon requiring considerable human effort. many psychological lexicons already exist in other languages and have played a considerable role in prior si research, see @xcite . investing further effort in lexicon creation may have further reduced the false negative rate. third, the lack of a benchmark dataset for the si detection task makes it difficult to compare with prior work and approaches. ## conclusion and future work accurate and early detection of users' suicide risk in text-based counseling services is essential to ensure that at-risk individuals are given timely and proper treatment. this paper provides an automatic approach to risk detection from chats in hebrew, a low-resource and morpholigically rich language. our approach adapted a generic hebrew language model by (i) adding out-of-vocabulary tokens, and (ii) performing additional pre-training of the lm on a masked language modeling task over the specific domain. finally, we fine-tuned the model for the suicide risk detection task. we combined this model with a lexicon of hand crafted suicide ideation phrases that were vetted by experts. our ensemble model outperformed several competitive approaches, including a generic language model and the stand-alone lexicon. our model performed consistently well for different demographics (age, gender). these encouraging results suggest the model can be deployed successfully, providing the much needed support to volunteers and health-care professionals in their mission of reducing suicide rates. in future work, we wish to integrate the discursive structure into the model, and include latent information about the cognitive state of the helpseeker.