Malta

Social anxiety represents a prevalent challenge in modern society, affecting individuals across personal and professional spheres. Left unaddressed, this condition can yield substantial negative consequences, impacting social interactions and performance. Further understanding its diverse physical and emotional symptoms becomes pivotal for comprehensive diagnosis and tailored therapeutic interventions. This study analyze prev lance and frequency of social anxiety symptoms taken from Mayo Clinic, exploring diverse human experiences from utilizing a large Reddit dataset dedicated to this issue. Leveraging these platforms, the research aims to extract insights and examine a spectrum of physical and emotional symptoms linked to social anxiety disorder. Upholding ethical considerations, the study maintains strict user anonymity within the dataset. By employing a novel approach, the research utilizes BART-based multi-label zero-shot classification to identify and measure symptom prevalence and significance in the form of probability score for each symptom under consideration. Results uncover distinctive patterns: &quot;Trembling&quot; emerges as a prevalent physical symptom, while emotional symptoms like &quot;Fear of being judged negatively&quot; exhibit high frequencies. These findings offer insights into the multifaceted nature of social anxiety, aiding clinical practices and interventions tailored to its diverse expressions.

EACL 2024

Prevalent Frequency of Emotional and Physical Symptoms in Social Anxiety using Zero Shot Classification: An Observational Study

social anxiety symptoms

zero shot classification

bart

Welcome to the largest national, multi-specialty research event **featuring research from 750 medical students, residents and fellows, and international medical graduates.**


#### Event Details
The 2022 American Medical Association Research Challenge includes a poster symposium hall and a semifinals hall with the top scored research. Log in at any time throughout the event to:
* **View posters and presentations** on a variety of topics and specialties
* **Score posters in the semifinals** to help decide the top five finalists (AMA member exclusive opportunity)
* **Connect with participants** and provide feedback to help advance their research

At the conclusion of this event, the AMA will announce the top five finalists who will advance to the AMA Research Challenge finals, present their research to an elite panel of judges and compete to win a $10,000 grand prize sponsored by Laurel Road.
 

#### Attend Live Forums
**Oct. 20, 6 — 7 p.m. Central**
 **How to plan a career in academic medicine** 
 Learn how to plan a career in academic medicine and get your questions answered. [Access Here](https://underline.io/events/363/lounge)
 
 **Speakers**
![](https://assets.underline.io/uploads/markdown_image/1/image/d5adb133dfc58eb0a6d469e9cedcd5d4.jpg)
Ricardo Correa, MD, EdD
![](https://assets.underline.io/uploads/markdown_image/1/image/5f9437ab87861538bf02b9ca7c0d926e.jpg)
Stuart M. Greenstein, MD
![](https://assets.underline.io/uploads/markdown_image/1/image/05b1b637c99286d558c371f8e8b185ce.jpg)
Stephanie Strohbeen, MD
 
 **Oct. 22, 11 a.m. — Noon Central**
 **How to get published**
 Learn tips to get your research published and get your questions answered. [Access Here](https://underline.io/events/363/lounge)
 
 **Speakers**
![](https://assets.underline.io/uploads/markdown_image/1/image/8ce6235e957d31cb5e6f9097cc6f6972.jpg)
Jessica Li, MD/PhD Candidate
![](https://assets.underline.io/uploads/markdown_image/1/image/9950ef2e0237f54cb3d8280334d42491.jpg)
Charles Lopresto, DO

### Sponsor
AMA would like to thank Laurel Road for sponsoring a $10,000 grand prize for the winner of the AMA Research Challenge. Laurel Road is not involved in the judging or scoring process to select the winner. The winner will be announced on Dec. 7 during the AMA Research Challenge finals. Payment of the grand prize will be directly from Laurel Road. See the AMA Research Challenge Official Rules for terms and conditions.

![](https://assets.underline.io/uploads/markdown_image/1/image/611e331d4a0eba0920f240cfc04b4d77.png)

**There is no registration fee for this event! **

Register to explore research in a variety of topics. AMA members get exclusive access to score posters in the semifinals.

**Please use the link below to register for free if you have not already done so:**

https://www.ama-assn.org/about/events/ama-research-challenge

Registration Is Required

AMA Research Challenge 2022

Welcome to the largest national, multi-specialty research event featuring research from 750 medical students, residents and fellows, and international medical graduates.


Social anxiety represents a prevalent challenge in modern society, affecting individuals across personal and professional spheres. Left unaddressed, this condition can yield substantial negative consequences, impacting social interactions and performance. Further understanding its diverse physical and emotional symptoms becomes pivotal for comprehensive diagnosis and tailored therapeutic interventions. This study analyze prev lance and frequency of social anxiety symptoms taken from Mayo Clinic, exploring diverse human experiences from utilizing a large Reddit dataset dedicated to this issue. Leveraging these platforms, the research aims to extract insights and examine a spectrum of physical and emotional symptoms linked to social anxiety disorder. Upholding ethical considerations, the study maintains strict user anonymity within the dataset. By employing a novel approach, the research utilizes BART-based multi-label zero-shot classification to identify and measure symptom prevalence and significance in the form of probability score for each symptom under consideration. Results uncover distinctive patterns: "Trembling" emerges as a prevalent physical symptom, while emotional symptoms like "Fear of being judged negatively" exhibit high frequencies. These findings offer insights into the multifaceted nature of social anxiety, aiding clinical practices and interventions tailored to its diverse expressions.

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. 
#### [Conference Handbook](https://drive.google.com/file/d/13Wn38q0yev6U3RTgnf4Tn5T9GuAuZ-xW/view?usp=sharing) *downloadable*

To access the EACL 2024 event page you need to register [**here**](https://acl.swoogo.com/EACL2024). 

Please register!

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.

The recognition of mental health's crucial significance has led to a growing interest in utilizing social media text data in current research trends. However, there remains a significant gap in the study of panic and anxiety on these platforms, despite their high prevalence and severe impact. In this paper, we address this gap by presenting a dataset consisting of 1,930 user posts from Quora and Reddit specifically focusing on panic and anxiety. Through a combination of lexical analysis, emotion detection, and writer attitude assessment, we explore the unique characteristics of each condition. To gain deeper insights, we employ a mental health-specific transformer model and a large language model for qualitative analysis. Our findings not only contribute to the understanding digital discourse on anxiety and panic but also provide valuable resources for the broader research community. We make our dataset, methodologies, and code available to advance understanding and facilitate future studies.

Comparing panic and anxiety on a dataset collected from social media

This paper addresses the quality of annotations in mental health datasets used for NLP-based depression level estimation from social media texts. While previous research relies on social media-based datasets annotated with binary categories, i.e. depressed or non-depressed, recent datasets such as D2S and PRIMATE aim for nuanced annotations using PHQ-9 symptoms. However, most of these datasets rely on crowd workers without the domain knowledge for annotation. Focusing on the PRIMATE dataset, our study reveals concerns regarding annotation validity, particularly for the lack of interest or pleasure symptom. Through reannotation by a mental health professional, we introduce finer labels and textual spans as evidence, identifying a notable number of false positives. Our refined annotations, to be released under a Data Use Agreement, offer a higher-quality test set for anhedonia detection. This study underscores the necessity of addressing annotation quality issues in mental health datasets, advocating for improved methodologies to enhance NLP model reliability in mental health assessments.

Your Model Is Not Predicting Depression Well And That Is Why: A Case Study of PRIMATE Dataset

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).

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

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.

Downloads

Next from EACL 2024

Comparing panic and anxiety on a dataset collected from social media

Similar lecture

Paraganglioma and Anxiety, A Physiologic Feedback Cycle: Case Report and Review of Literature

.css-70qvj9{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}Downloads

Next from EACL 2024

Comparing panic and anxiety on a dataset collected from social media

Similar lecture

Paraganglioma and Anxiety, A Physiologic Feedback Cycle: Case Report and Review of Literature

Downloads