  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.
