Multi-task Learning for Personal Health Mention Detection on Social Media
This work addresses the problem of health surveillance for public health monitoring by enhancing detection accuracy, though it appears incremental as it builds on existing multitask learning methods.
The paper tackled the challenge of detecting personal health mentions on social media by using a multitask learning framework that incorporates emotion detection as an auxiliary task, resulting in significant improvements over a strong state-of-the-art baseline.
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.