Detecting Suicide Risk Patterns using Hierarchical Attention Networks with Large Language Models (2024)

Abstract: Suicide has become a major public health and social concern in the world . This Paper looks into a method through use of LLMs (Large Lan- guage Model) to extract the likely reason for a person to attempt suicide , through analysis of their social media text posts detailing about the event , using this data we can extract the rea- son for the cause such mental state which can provide support for suicide prevention. This submission presents our approach for CLPsych Shared Task 2024. Our model uses Hierarchi- cal Attention Networks (HAN) and Llama2 for finding supporting evidence about an individ- ual’s suicide risk level.

## introduction suicide is a common and very serious concern that affects many lives globally @xcite . detecting signs of suicide early is important to provide timely help and support to those at risk. even though many current methods for identifying suicide risk exist, they have their limitations in terms of efficiency and accuracy. traditional methods focus on factors such as psychiatric diagnoses, agitation, past suicidal behavior or even self-reported questionnaire surveys @xcite . however, these methods sometimes struggle to predict suicidal thoughts accurately, and there's a need for more effective tools. social media platforms have become a valuable source for understanding and identifying suicide risk. people often share their thoughts and emotions on platforms like twitter, facebook, and reddit. diagnosing these posts on these platforms can be very helpful to get insights into the lives of the individuals who might be struggling with suicidal thoughts. in recent years, there's been exciting progress in the use of technology to enhance suicide detection, particularly the use artificial intelligence. many researches have used various machine learning @xcite and deep learning algorithms @xcite to detect signs of suicide risk with varying levels of success. improving the current models could end up being very helpful to prevent many suicide cases. based on reddit data, in this paper we use llms for extracting suicidal thoughts from the user. llms use advanced natural language processing algorithms to analyze vast amounts of textual data, including social media posts, to identify patterns and linguistic cues associated with suicidal thoughts. by leveraging the power of llms, researchers and mental health professionals can develop more sophisticated and accurate tools for detecting and understanding the individual. ## related work there is a rising number of research being done in suicide risk detection. this has been a key focus in the field of natural language processing. this task has been done using a lot of methods, but the key focus keeps evolving over time as the field of machine learning and artificial intelligence keeps expanding. various machine learning algorithms have been used for this task like long short term memory (lstm) and convolutional neural networks (cnn) in @xcite or ensemble learning methods using convolutional neural networks (cnn) and xgboost used by @xcite . there has also been an increase in the use of transformer based models @xcite . recent work on large language models (llms) suggest that they can perform well on nlp tasks such as information extraction @xcite and question answering @xcite which could help us in identifying evidence supporting individual's suicide risk level from a given social media post. we used a hierarchical attention network (han) @xcite in our model to capture the importance of the words and the sen-tences of the post to find the portions of the text which indicate the presence of suicide risk. ## dataset this paper discusses our involvement in clpsych 2024 shared task @xcite . the problem statement was to use an open source llm to provide evidence for the assigned suicide risk level of a person on the basis of their linguistic content. our task was to highlight the parts of the text which indicate evidence of suicide risk and explain the assignment of a particular suicide risk level using our model. the dataset we used is from the 2019 clpsych shared task a @xcite , @xcite (university of maryland reddit suicidality dataset, version 2). this includes a collection of data from reddit posts within the r/suicidewatch community. a careful selection process is employed to focus exclusively on posts where individuals openly share personal experiences related to suicide attempts. the dataset includes reddit users and their r/suicidewatch posts, alongside their suicide risk levels in four classes: no, low, moderate and severe risk. however we were asked to exclude posts and users labeled as no risk. the task participants were required to sign data sharing agreements and abide by ethical practice during the competition. ## methodology the architecture of the process followed in this paper can be mainly divided into two parts ,first being use of hierarchical attention modeling to get highlights from the posts from the user and then using these highlights with the post , using this as llama-2 llm for generating the summarized reason for suicide attempt . ## results the results of our methodology demonstrate strong performance, with high precision and recall values indicating accurate and comprehensive summarization of suicide-related content within the dataset. the generated summaries not only provide meaningful insights into likely reasons behind suicide attempts but also maintain interpretability. overall, these results suggest the potential of our method in extracting and understanding sensitive content within online communities, contributing to both research and mental health support systems. ## ethics and limitations we obtained our dataset from the university of maryland and adhered to ethical standards throughout the research process. the dataset, comprising sensitive information, has been handled with utmost confidentiality, and no sharing has occurred to maintain participant privacy and comply with ethical guidelines. time constraints posed challenges in conducting a more extensive research. this constraint affected the depth of our analysis and the ability to explore additional variables. despite these limitations, we believe our study provides valuable insights within the given constraints.