Thailand

This is the demonstration of systems and results of our team’s participation in the Social Medi- cal Mining for Health (SMM4H) 2024 Shared Task. Our team participated in two tasks: Task 1 and Task 5. Task 5 requires the detection of tweet sentences that claim children’s medi- cal disorders from certain users. Task 1 needs teams to extract and normalize Adverse Drug Event terms in the tweet sentence. The team selected several Pre-trained Language Models and generative Large Language Models to meet the requirements. Strategies to improve the per- formance include cloze test, prompt engineer- ing, Low Rank Adaptation etc. The test result of our system has an F1 score of 0.935, Preci- sion of 0.954 and Recall of 0.917 in Task 5 and an overall F1 score of 0.08 in Task 1.

ACL 2024

PolyuCBS at SMM4H 2024: LLM-based Medical Disorder and Adverse Drug Event Detection with Low-rank Adaptation

medical disorder detection

adverse drug event normalization

adverse drug event extraction

twitter

social media

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workshop paper

The advent of Large Language Models (LLMs) such as Generative Pre-trained Transformers (GPT-4) mark a transformative era in Natu- ral Language Generation (NLG). These mod- els demonstrate the ability to generate coher- ent text that closely resembles human-authored content. They are easily accessible and have become invaluable tools in handling various text-based tasks, such as data annotation, report generation, and question answering. In this pa- per, we investigate GPT-4’s ability to discern between data it has annotated and data anno- tated by humans, specifically within the context of tweets in the medical domain. Through ex- perimental analysis, we observe GPT-4 outper- form other state-of-the-art models. The dataset used in this study was provided by the SMM4H (Social Media Mining for Health Research and Applications) shared task. Our model achieved an accuracy of 0.51, securing a second rank in the shared task.

Deloitte at #SMM4H 2024: Can GPT-4 Detect COVID-19 Tweets Annotated by Itself?

Many individuals affected by Social Anxiety Disorder turn to social media platforms to share their experiences and seek advice. This in- cludes discussing the potential benefits of en- gaging with outdoor environments. As part of #SMM4H 2024, Shared Task 3 focuses on classifying the effects of outdoor spaces on social anxiety symptoms in Reddit posts. In our contribution to the task, we explore the ef- fectiveness of domain-specific models (trained on social media data – SocBERT) against general domain models (trained on diverse datasets – BERT, RoBERTa, GPT-3.5) in pre- dicting the sentiment related to outdoor spaces. Further, we assess the benefits of augment- ing sparse human-labeled data with synthetic training instances and evaluate the complemen- tary strengths of domain-specific and general classifiers using an ensemble model. Our re- sults show that (1) fine-tuning small, domain- specific models generally outperforms large general language models in most cases. Only one large language model (GPT-4) exhibits per- formance comparable to the fine-tuned mod- els (52% F1). Further, we find that (2) syn- thetic data does improve the performance of fine-tuned models in some cases, and (3) mod- els do not appear to complement each other in our ensemble setup.

IMS_medicALY at #SMM4H 2024: Detecting Impacts of Outdoor Spaces on Social Anxiety with Data Augmented

Social Anxiety Disorder (SAD) is a common condition, affecting a significant portion of the population. While research suggests spending time in nature can alleviate anxiety, the specific impact on SAD remains unclear. This study ex- plores the relationship between discussions of outdoor spaces and social anxiety on social me- dia. We leverage transformer-based and large language models (LLMs) to analyze a social media dataset focused on SAD. We developed three methods for the task of predicting the effects of outdoor spaces on SAD in social me- dia. A two-stage pipeline classifier achieved the best performance of our submissions with results exceeding baseline performance.

LAMA at SMM4H 2024: Experimenting with Transformer-based and Large Language Models for Classifying Effects of Outdoor Spaces on Social Anxiety in Social Media Data

The paper presents two distinct approaches to Task 6 of the SMM4H’24 workshop: extracting self-reported exact age information from social media posts across platforms. This research task focuses on developing methods for au- tomatically extracting self-reported ages from posts on two prominent social media platforms: Twitter (now X) and Reddit. The work lever- ages two ways, one Mistral-7B-Instruct-v0.2 Large Language Model (LLM) and another pre- trained language model BERTweet, to achieve robust and generalizable age classification, sur- passing limitations of existing methods that rely on predefined age groups. The proposed mod- els aim to advance the automatic extraction of self-reported exact ages from social media posts, enabling more nuanced analyses and in- sights into user demographics across different platforms.

SMM4H’24 Task6 : Extracting Self-Reported Age with LLM and BERTweet: Fine-Grained Approaches for Social Media Text

This paper evaluates the performance of "AAST-NLP" in the Social Media Mining for Health (SMM4H) Shared Tasks 3 and 6, where more than 20 teams participated in each. We leveraged state-of-the-art transformer-based models, including Mistral, to achieve our re- sults. Our models consistently outperformed both the mean and median scores across the tasks. Specifically, an F1-score of 0.636 was achieved in classifying the impact of outdoor spaces on social anxiety symptoms, while an F1-score of 0.946 was recorded for the classifi- cation of self-reported exact ages

AAST-NLP@#SMM4H’24: Finetuning Language Models for Exact Age Classification and Effect of Outdoor Spaces on Social Anxiety

This paper presents our work for the Task 5 of the Social Media Mining for Health Applica- tions 2024 Shared Task - Binary classification of English tweets reporting children’s medical disorders. In this paper, we present and com- pare multiple approaches for automatically clas- sifying tweets from parents based on whether they mention having a child with attention- deficit/hyperactivity disorder (ADHD), autism spectrum disorders (ASD), delayed speech, or asthma. We use ensemble of various BERT- based models trained on provided dataset that yields an F1 score of 0.901 on the test data.

CogAI@SMM4H 2024: Leveraging BERT-based Ensemble Models for Classifying Tweets on Developmental Disorders

This study describes the approach of Team ADE Oracle for Task 1 of the Social Media Mining for Health Applications (#SMM4H) 2024 shared task. Task 1 challenges partic- ipants to detect adverse drug events (ADEs) within English tweets and normalize these men- tions against the Medical Dictionary for Regu- latory Activities standards. Our approach uti- lized a two-stage NLP pipeline consisting of a named entity recognition model, retrained to recognize ADEs, followed by vector similar- ity assessment with a RoBERTa-based model. Despite achieving a relatively high recall of 37.4% in the extraction of ADEs, indicative of effective identification of potential ADEs, our model encountered challenges with preci- sion. We found marked discrepancies between recall and precision between the test set and our validation set, which underscores the need for further efforts to prevent overfitting and en- hance the model’s generalization capabilities for practical applications.

ADE Oracle at #SMM4H 2024: A Two-Stage NLP System for Extracting and Normalizing Adverse Drug Events from Tweets

The proliferation of LLMs in various NLP tasks has sparked debates regarding their reliability, particularly in annotation tasks where biases and hallucinations may arise. In this shared task, we address the challenge of distinguishing annotations made by LLMs from those made by human domain experts in the context of COVID-19 symptom detection from tweets in Latin American Spanish. This paper presents BrainStorm @ iREL’s approach to the SMM4H 2024 Shared Task, leveraging the inherent topi- cal information in tweets, we propose a novel approach to identify and classify annotations, aiming to enhance the trustworthiness of anno- tated data.

BrainStorm @ iREL at #SMM4H 2024: Leveraging Translation and Topical Embeddings for Annotation Detection in Tweets

We describe the methods and results of our submission to the 9th Social Media Mining for Health Research and Applications (SMM4H) 2024 shared tasks 4 and 5. Task 4 involved extracting the clinical and social impacts of non-medical substance use and task 5 focused on the binary classification of tweets reporting children’s medical disorders. We employed encoder language models and their ensembles, achieving the top score on task 4 and a high score for task 5.

UKYNLP@SMM4H2024: Language Model Methods for Health Entity Tagging and Classification on Social Media (Tasks 4 & 5)

Adverse drug events (ADEs) pose major pub- lic health risks, with traditional reporting sys- tems often failing to capture them. Our pro- posed pipeline, called Deep-LLMADEminer, used natural language processing approaches to tackle this issue for #SMM4H 2024 shared task 1. Using annotated tweets, we built a three part pipeline: RoBERTa for classification, GPT- 4-turbo for span extraction, and BioBERT for normalization. Our models achieved F1-scores of 0.838, 0.306, and 0.354, respectively, of- fering a novel system for Task 1 and similar pharmacovigilance tasks.

LHS712_ADENotGood at #SMM4H 2024 Task 1: Deep-LLMADEminer: A deep learning and LLM pharmacovigilance pipeline for extraction and normalization of adverse drug event mentions on Twitter

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Deloitte at #SMM4H 2024: Can GPT-4 Detect COVID-19 Tweets Annotated by Itself?

Similar lecture

BIT@UA at #SMM4H 2024 Tasks 1 and 5: finding adverse drug events and children’s medical disorders in English tweets