DILAB at # SMM 4 H 2024: R o BERT a Ensemble for Identifying Children’s Medical Disorders in E nglish Tweets (2024)

Abstract: This paper details our system developed for the 9th Social Media Mining for Health Research and Applications Workshop (SMM4H 2024), addressing Task 5 focused on binary classification of English tweets reporting children’s medical disorders. Our objective was to enhance the detection of tweets related to children’s medical issues. To do this, we use various pre-trained language models, like RoBERTa and BERT. We fine-tuned these models on the task-specific dataset, adjusting model layers and hyperparameters in an attempt to optimize performance. As we observe unstable fluctuations in performance metrics during training, we implement an ensemble approach that combines predictions from different learning epochs. Our model achieves promising results, with the best-performing configuration achieving F1 score of 93.8% on the validation set and 89.8% on the test set.

## introduction health informatics research often involves analyzing social media data from platforms like twitter, facebook, and reddit to understand public sentiment on health-related topics. the 9th edition of the social media mining for health research and applications workshop (smm4h 2024) @xcite is dedicated to advancing this area. researchers at smm4h 2024 aim to contribute by exploring topics like deriving health trends from social media, classifying health-related messages, identifying health-related or medical terms and monitoring diseases using social media content. we decided to engage in task 5 of this workshop, focusing on the binary classification of english tweets reporting children's medical disorders. this task aims to refine methods for accurately identifying and categorizing tweets related to pediatric health conditions. it aligns with broader efforts in health informatics research to understand public 2 system description ## evaluation in the results on the test set are outlined in ## conclusion studying social media data continues to be vital in health informatics research, providing valuable insights into public sentiment on health-related topics. using pre-trained language models like roberta and bert, we obtain text embeddings and fine-tuned them using mlp classifiers on the task dataset with strategic adjustments to model layers and hyperparameters, enhancing performance. additionally, we applied an ensemble approach on epochs to ensure performance stability. our top models achieved 0.9316 and 0.9382 f1-macro on validation and 0.898 and 0.892 on the test set, demonstrating its effectiveness.