Malta

We present a novel task that can elucidate the connection between anxiety and ADHD; use Transformers to make progress toward solving a task that is not solvable by keyword-based classifiers; and discuss a method for visualization of our classifier illuminating the connection between anxiety and ADHD presentations. Up to approximately 50% of adults with ADHD may also have an anxiety disorder and approximately 30% of adults with anxiety may also have ADHD. Patients presenting with anxiety may be treated for anxiety without ADHD ever being considered, possibly affecting treatment. We show how data that bears on ADHD that is comorbid with anxiety can be obtained from social media data, and show that Transformers can be used to detect a proxy for possible comorbid ADHD in people with anxiety symptoms. We collected data from anxiety and ADHD online forums (subreddits). We identified posters who first started posting in the Anxiety subreddit and later started posting in the ADHD subreddit as well. We use this subset of the posters as a proxy for people who presented with anxiety symptoms and then became aware that they might have ADHD. We fine-tune a Transformer architecture-based classifier to classify people who started posting in the Anxiety subreddit and then started posting in the ADHD subreddit vs. people who posted in the Anxiety subreddit without later posting in the ADHD subreddit. We show that a Transformer architecture is capable of achieving reasonable results (76% correct for RoBERTa vs. under 60\% correct for the best keyword-based model, both with 50% base rate).

EACL 2024

Detecting a Proxy for Potential Comorbid ADHD in People Reporting Anxiety Symptoms from Social Media Data

nosology

adhd

anxiety

transformers

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AAPT SM 21

workshop paper

Welcome to the 18th Conference of the European Chapter of the Association for Computational Linguistics (EACL). Continuing its mission of expanding and involving the science community of all European countries, EACL has selected the Malta community for the 18th EACL. Considering the importance of physical interaction among researchers, the conference will be held at the Hotel Radisson Blu, St. Julians, in Malta, from 17 to 22 of March, 2024. The conference will also feature the possibility to participate virtually. As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language. 
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As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

This paper presents our approach to the CLPsych 2024 shared task: utilizing large language models (LLMs) for finding supporting evidence about an individual's suicide risk level in Reddit posts. Our framework is constructed around an LLM with knowledge self-generation and output refinement. The knowledge self-generation process produces task-related knowledge which is generated by the LLM and leads to accurate risk predictions. The output refinement process, later, with the selected best set of LLM-generated knowledge, refines the outputs by prompting the LLM repeatedly with different knowledge instances interchangeably. We achieved highly competitive results comparing to the top-performance participants with our official recall of 93.5%, recall--precision harmonic-mean of 92.3%, and mean consistency of 96.1%.

Team ISM at CLPsych 2024: Extracting Evidence of Suicide Risk from Reddit Posts with Knowledge Self-Generation and Output Refinement using A Large Language Model

Monitoring and predicting the expression of suicidal risk in individuals' social media posts is a central focus in clinical NLP. Yet, existing approaches frequently lack a crucial explainability component necessary for extracting evidence related to an individual's mental health state. We describe the CSIRO Data61 team's evidence extraction system submitted to the CLPsych 2024 shared task. The task aims to investigate the zero-shot capabilities of open-source LLM in extracting evidence regarding an individual's assigned suicide risk level from social media discourse. The results are assessed against ground truth evidence annotated by psychological experts, with an achieved recall-oriented BERTScore of 0.919. Our findings suggest that LLMs showcase strong feasibility in the extraction of information supporting the evaluation of suicidal risk in social media discourse. Opportunities for refinement exist, notably in crafting concise and effective instructions to guide the extraction process.

Exploring Instructive Prompts for Large Language Models in the Extraction of Evidence for Supporting Assigned Suicidal Risk Levels

This study explores the use of Large Language Models (LLMs) to analyze text comments from Reddit users, aiming to achieve two primary objectives: firstly, to pinpoint critical excerpts that support a predefined psychological assessment of suicidal risk; and secondly, to summarize the material to substantiate the preassigned suicidal risk level. The work is circumscribed to the use of "open-source" LLMs that can be run locally, thereby enhancing data privacy. Furthermore, it prioritizes models with low computational requirements, making it accessible to both individuals and institutions operating on limited computing budgets. The implemented strategy only relies on a carefully crafted prompt and a grammar to guide the LLM's text completion. Despite its simplicity, the evaluation metrics show outstanding results, making it a valuable privacy-focused and cost-effective approach. This work is part of the Computational Linguistics and Clinical Psychology (CLPsych) 2024 shared task.

Psychological Assessments with Large Language Models: A Privacy-Focused and Cost-Effective Approach

Large language model classifiers do not directly offer transparency: it is not clear why one class is chosen over another. In this work, summaries explaining the suicide risk level assigned using a fine-tuned mental-roberta-base model are generated from key phrases extracted using SHAP explainability using Mistral-7B. The training data for the classifier consists of all Reddit posts of a user in the University of Maryland Reddit Suicidality Dataset, Version 2, with their suicide risk labels along with selected features extracted from each post by the Linguistic Inquiry and Word Count (LIWC-22) tool. The resulting model is used to make predictions regarding risk on each post of the users in the evaluation set of the CLPsych 2024 shared task, with a SHAP explainer used to identify the phrases contributing to the top scoring, correct and severe risk categories. Some basic stoplisting is applied to the extracted phrases, along with length based filtering, and a locally run version of Mistral-7B-Instruct-v0.1 is used to create summaries from the highest value (based on SHAP) phrases.

Incorporating Word Count Information into Depression Risk Summary Generation: INF@UoS CLPsych 2024 Submission

This paper explores the use of Large Language Models (LLMs) in analyzing social media content for mental health monitoring, specifically focusing on detecting and summarizing evidence of suicidal ideation. We utilized LLMs Mixtral7bx8 and Tulu-2-DPO-70B, applying diverse prompting strategies for effective content extraction and summarization. Our methodology included detailed analysis through Few-shot and Zero-shot learning, evaluating the ability of Chain-of-Thought and Direct prompting strategies. The study achieved notable success in the CLPsych 2024 shared task (ranked top for the evidence extraction task and second for the summarization task), demonstrating the potential of LLMs in mental health interventions and setting a precedent for future research in digital mental health monitoring.

Extracting and Summarizing Evidence of Suicidal Ideation in Social Media Contents Using Large Language Models

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.

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

For numerous years, researchers have employed social media data to gain insights into users' mental health. Nevertheless, the majority of investigations concentrate on categorizing users into those experiencing depression and those considered healthy, or on detection of suicidal thoughts. In this paper, we aim to extract evidence of a pre-assigned gold label. We used a suicidality dataset containing Reddit posts labeled with the suicide risk level. The task is to use Large Language Models (LLMs) to extract evidence from the post that justifies the given label. We used Meta Llama 7b and lexicons for solving the task and we achieved a precision of 0.96.

Using Large Language Models (LLMs) to Extract Evidence from Pre-Annotated Social Media Data

In this article, we introduce a new method for analyzing and summarizing posts from r/SuicideWatch on Reddit, overcoming the limitations of current techniques in processing complex mental health discussions online. Existing methods often struggle to accurately identify and contextualize subtle expressions of mental health problems, leading to inadequate support and intervention strategies. Our approach combines the open-source Large Language Model (LLM), fine-tuned with health-oriented knowledge, to effectively process Reddit posts. We also design prompts that focus on suicide-related statements, extracting key statements, and generating concise summaries that capture the core aspects of the discussions. The preliminary results indicate that our method improves the understanding of online suicide-related posts compared to existing methodologies.

XinHai@CLPsych 2024 Shared Task: Prompting Healthcare-oriented LLMs for Evidence Highlighting in Posts with Suicide Risk

Despite the increasing demand for AI-based mental health monitoring tools, their practical utility for clinicians is limited by the lack of interpretability. The CLPsych 2024 Shared Task (Chim et al., 2024) aims to enhance the interpretability of Large Language Models (LLMs), particularly in mental health analysis, by providing evidence of suicidality through linguistic content. We propose a dual-prompting approach: (i) Knowledge-aware evidence extraction by leveraging the expert identity and a suicide dictionary with a mental health-specific LLM; and (ii) Evidence summarization by employing an LLM-based consistency evaluator. Comprehensive experiments demonstrate the effectiveness of combining domain-specific information, revealing performance improvements and the approach's potential to aid clinicians in assessing mental state progression.

A Dual-Prompting for Interpretable Mental Health Language Models

This paper describes the Unibuc Archaeology team work for CLPsych's 2024 Shared Task that involved finding evidence within the text supporting the assigned suicide risk level. Two types of evidence were required: highlights (extracting relevant spans within the text) and summaries (aggregating evidence into a synthesis). Our work focuses on evaluating Large Language Models (LLM) as opposed to an alternative method that is much more memory and resource efficient. The first approach employs an LLM that is used for generating the summaries and is guided to provide sequences of text indicating suicidal tendencies through a processing chain for highlights. The second approach involves implementing a good old-fashioned machine learning tf-idf with a logistic regression classifier, whose representative features we use to extract relevant highlights.

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