Combining Psychological Theory with Language Models for Suicide Risk Detection (2023)

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 suicide accounts for more than 700,000 lives lost across the world every year. it is the second leading cause of death for adolescents and adults from 15 to 29 years of age in many countries. a key effort in suicide prevention is to identify individuals at risk of suicide as early as possible @xcite . in the past decade, online counseling services for mental health support have become commonplace in many countries, providing chat support and guidance to at-risk individuals (see fictitious example in figure 1 ). online counseling services aim to provide mental support and address a variety of mental health crises through specialist counselors. these counselors are trained to detect suicide risk during conversations and intervene quickly as needed. these services have experienced tremendous growth in traffic since the commencement of the covid pandemic @xcite . any kind of technological support to help counselors in this critical task can potentially save lives. this paper provides a computational model for detection of suicide risk from anonymous textbased discussions between help-seekers and counselors. our data is taken from an online counseling service in a low-resource language (hebrew). there are several challenges towards solving suicide risk detection in our setting: state of the art pre-trained language models for suicide prevention usually focus on posts from social media which are very different in structure from online conversations between counselors and help-seekers. existing works that do consider conversations in this domain ignore the conversation structure or are limited in the size of the conversation they consider. also, the set of nlp resources available for low-resource languages is extremely limited when compared to english. to address this gap, we present a hierarchical language model called sr-bert that includes a base layer for encoding the conversation text and an additional layer for capturing aspects of conversation structure. the hierarchical structure of sr-bert encodes each of the messages in the conversation separately and is not limited by the size of the conversation. we hypothesized that incorporating knowledge from suicide risk theory as part of the pre-training step can improve downstream performance of the detection model. to this end, we develop a new domain knowledge-based pretraining step that embeds a suicide risks factor lexicon (srf) into sr-bert. the srf lexicon was created by a team of psychologists who are experts on suicide risk theory and prevention. in empirical studies, sr-bert significantly outperforms alternative classifiers for suicide risk (sr) detection, including the state-of-the-art @xcite . we show that adding the domainexpert information to sr-bert plays a critical part in its performance. in particular, it obtained consistently better performance than @xcite when processing different portions of the conversation. these findings suggest that sr-bert can perform well in the field when analyzing conversations in real-time. we extend the state-of-the-art hierarchical language models to combine conversation structure and expert-based knowledge in the pretraining step. we show this approach leads to significant increases in performance for detecting suicide risk from chat conversations. ## related work this paper relates to past studies in suicide risk detection in online settings, representing domain knowledge and conversation structure in deep language models and nlp tools for low-resource languages. we expand on each of these topics in turn. for a review on using machine learning in suicide prevention, we refer the reader to @xcite . the majority of work using machine learning to predict suicide risk analyzes posts from social media @xcite @xcite @xcite . recent works in this social media suicide prediction space includes @xcite who used an lstm with an additional attention layer to predict sr from social media posts, and @xcite who combined a generic bert model with predefined rules for scoring suicide risk in social media. additionally, @xcite showed that psychological questionnaires can improve the performance of neural networks to identify at-risk individuals from facebook posts. we significantly differ from the social media setting in our focus on conversations from online coun-seling services, where messages are significantly longer than social media posts, and messages are part of a conversational structure and exhibit psychological dynamics. we show that capturing these aspects in the conversation model is necessary for recognizing sr in our setting. there are few works on suicide detection in online counseling conversations, but none of these reasons about the conversation structure in the session. most relevant to our approach is the model by @xcite who combined a pre-trained language model based on bert @xcite with a lexicon of suicide terms that were manually extracted from conversations. this model was able to represent only part of the conversation (512 tokens) and ignored the input from the counselor. our sr-bert model used a lexicon extracted from psychological theory, which was embedded in the pre-training process. the model significantly outperforms that of @xcite on the same dataset, both for entire conversations as well as when considering early detection on parts of the conversation. we mention two approaches for detecting sr in counseling services that did not consider early detection. @xcite combined a word2vec representation of suicide concepts with a bi-directional lstm network for sr prediction in korean online counseling service. each side of the conversation was represented by an independent bi-lstm. this approach used a knowledge graph to represent a psychological lexicon which may be more timeconsuming for human experts to construct. our model is shown to outperform a baseline using a similar representation (doc2vec) on our dataset. @xcite used tf-idf embedding with xgboost to predict sr in transcribed phone calls from an english counseling service. this model did not use a lexicon. there is ample evidence on the benefits of incorporating domain knowledge in language models for downstream tasks @xcite @xcite @xcite . notable examples include @xcite and @xcite who showed that using lexicon-based features can improve machine learning prediction of suicide risk in chinese blogs. they use lexicons to map terms from online discussions to clinically relevant sets of categories. we extend these approaches by presenting a new method for incorporating domain knowledge in the pre-training phase of deep learning models. in general, nlp models and solutions for lowresource languages are extremely limited. in hebrew, two pre-trained language models were published, hebert @xcite and alephbert @xcite . we used aleph-bert which is freely available and was trained on a larger dataset than hebert and was able to outperform hebert on a variety of natural language tasks. we are first to use hierarchical transformer architecture to model conversation structures in a low-resource language. ## the sahar domain sahar (hebrew acronym for online mental health support foot_0 ) was established in 2000 and is the leading internet-based emotional support and suicide prevention organization in israel. it provides anonymous, confidential, and free crisis support via a chat hotline (in hebrew and in arabic). the organization handles more than 40, 000 chat sessions per year, and these numbers have increased significantly during the covid-19 pandemic @xcite . sahar counselors are volunteers who receive year-round guidance and supervision from a team of mental health professionals. shifts take place in the evening hours and are accompanied by trained therapists who monitor the conversations and provide professional support to counselors as needed. during the shifts, counselors work in a high-stress environment and usually handle multiple chat sessions in parallel at any given time. counselors provide a written summary of each of their conversations, as well as indicate whether the conversation exhibits suicide risk. the sahar corpus contains more than 40, 000 chat sessions (conversations) that took place over the span of five years @xcite @xcite @xcite . each conversation includes the messages generated by the helpseekers and the counselors, ordered by time signatures. to validate the sr labels, a sample of 600 conversations (300 positive sr, 300 negative sr) was labeled separately by clinical psychologists with expertise in suicide theory. the krippendorff's α for inter-annotator agreement between the psychologists and the sr label in the conversation is 0.766, which is en par with other works. we note that the inconsistencies found in the samples were debated by the psychologists and resolved in the data set. ## the srf psychological lexicon as part of our research, a team of psychology experts from a national center for suicide prevention in israel has constructed a suicide-risk factors lexicon (srf) in hebrew that is based on psychological theory. the srf lexicon contains terms relating to personal and situational variables associated with an increase in suicidal thinking, based on valid selfreport questionnaires in the psychological and psychiatric literature @xcite @xcite . each of the 3,094 sentences in the lexicon belonged to one of 25 categories. specifically, terms relating to depression are taken from the patient health questionnaire depression module (phq-9) @xcite . terms relating to a sense of burdensomeness are taken from the interpersonal needs questionnaire (inq) @xcite . terms relating to a sense of hopelessness are taken from the beck hopelessness scale @xcite . terms relating to suicide behavior were taken from the columbia questionnaire @xcite which is a standard tool to measure suicide risk. examples of sentences for the category "perceived burdensomeness" (translated) included sentences such as "better without me", "i am a burden", "i spoil everything for my spouse"; and the lexicon category "explicit suicide mentions" contains phrases such as: "to die", "to commit suicide", "kill myself" etc. ## the sr-bert language model our main contribution is sr-bert, a two-layer hierarchical language model that extends the generic dialogbert @xcite to reason about conversation structure in suicide risk prediction settings and harness psychological domain knowledge. the sr-bert architecture is shown in figure 2(a). the architecture is composed of two part: a transformer based layer performing message encoding, and on top of it an additional transformer layer, which captures conversation structure, named context encoder transformer. the base layer uses the alephbert @xcite pre-trained language model to encode each message in the dialogue to a vector. the received message encoding is then combined with speaker role representation (help-seeker vs. counselor) to capture important conversation aspects such as turntaking. the context encoder transformer is a transformer based encoder applied at the message level (instead of the single token level) which transforms the series of message vectors into a contextsensitive repression of the conversation. the context encoder transformer included 12 attention layers, and 12 hidden layers, each with a vector size of 780. the hidden layer size is 780 rather than 768 in alephbert to account for the additional speaker role encoding. the hierarchical structure of the architecture enables the model to capture multiple messages including turn exchanges and speaker roles. furthermore, it enable the encoding of each message independently, thus avoiding the need to truncate conversations (due to alephbert's 512 token limit) as in past work. ## empirical methodology we randomly split the labeled sahar dataset to a train (70%) validation (15%) and test (15%) sets. these data sets were used throughout the experiments described in the following section. the validation set was used for training model hyper parameters. we follow prior work in evaluating model performance using roc-auc which is widely employed in suicide detection research @xcite . additionally, we report on the f2-score @xcite for predicting the positive sr label. this measure concentrates on reducing false negatives (rather than false positives) and is thus well suited for sr detection where missing a positive class has life threatening implications. we compare sr-bert with ssk to the following baseline models: 6.1 sr-bert w.o.ssk this model omits the ssk pre-training task from sr-bert w. ssk. apart from the ssk pretraining task this model is identical to sr-bert w. ssk. including the hierarchical structure and pre-training on the other 3 tasks. ## results we first present the performance of the sr-bert model in predicting sr on labeled conversations compared to the proposed baselines. results are then reported for early sr detection, when increasing percentages of conversation information are available. ## conclusion and future work this work has provided a new automatic approach for suicide risk detection in online conversations between help-seekers and counselors. early detection of at-risk individuals is a key goal of suicide prevention. our approach extends the state-of-theart in deep language modeling by 1) incorporating domain knowledge relevant to suicide risk detection as part of the pre-training step; 2) reasoning about the structure of the conversation between help-seekers and counselors; 3) adapting to a lowresource language (hebrew). the presented approach was able to significantly outperform the state-of-the-art approaches when detecting sr from complete conversations, as well as early detection when only part of the conversation is available. these results suggest the model may be able to support the work of counselors in real chat sessions, alerting them in real-time to at-risk individuals and enabling quick and focused responses. for future work, we intend to improve our approach by capturing more aspects of conversations, such as prosody @xcite as well as model the mental state dynamics of the help-seeker. we are also extending the model with explanations to be able to provide justifications for predictions made and point to key exchanges and phrases that triggered specific predictions.