Work

BioNLP at #SMM4H-HeaRD 2026 Task 3 Estimating Flu Vaccine Effectiveness: A Temporal-Aware Fine-Tuning and Similarity-Based Few-Shot Prompting Approach

Year: 2026

Type: conference-paper

Abstract: This paper presents our systems for the SMM4H 2026 shared task on flu-related tweet classification across two subtasks: flu vaccination status and flu test outcome classification.For each subtask, we evaluate two approaches: fine-tuning BERTweet-large with a temporal-aware architecture, cross-validation ensembling, and regularization techniques, and a GPT-4o few-shot prompting system with similarity-based dynamic example retrieval, chain-of-thought reasoning and contrastive label ranking.Fine-tuning proves superior for the flu vaccination subtask (micro-F1: 87.90%), where sufficient and relatively balanced training data is available, while few-shot prompting performs better for the flu test subtask (micro-F1: 95.74%), where limited and heavily imbalanced training data renders fine-tuning less effective.

Source: Social Media Mining for Health Research and Applications Workshop and Shared Tasks (SMM4H)

DOI: 10.18653/v1/2026.smm4h-1.41

Author: Irina Patularu

Institution: University of Bucharest

Language: English

Topic: Sentiment Analysis and Opinion Mining

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

OpenAlex ID: W7166815229