Multi-task Learning for Personal Health Mention Detection on Social Media

TL;DR

This paper introduces a multi-task learning approach that leverages emotion detection as an auxiliary task to improve the detection of personal health mentions on social media, addressing data annotation challenges.

Contribution

It presents a novel multi-task learning framework that incorporates emotion detection to enhance health mention detection performance.

Findings

Significant improvement over baseline models

Effective use of emotion detection as auxiliary task

Enhanced detection accuracy across multiple datasets

Abstract

Detecting personal health mentions on social media is essential to complement existing health surveillance systems. However, annotating data for detecting health mentions at a large scale is a challenging task. This research employs a multitask learning framework to leverage available annotated data from a related task to improve the performance on the main task to detect personal health experiences mentioned in social media texts. Specifically, we focus on incorporating emotional information into our target task by using emotion detection as an auxiliary task. Our approach significantly improves a wide range of personal health mention detection tasks compared to a strong state-of-the-art baseline.