2022/12/09 by Olanrewaju Tahir Aduragba, Jialin Yu, Aduragba, Olanrewaju Tahir +3
Computer Science · Psychology · #Artificial Intelligence (cs.AI) #Artificial intelligence #Complement (music) #Computation and Language (cs.CL) #Computer science #Data science #FOS: Computer and information sciences #Focus (optics) #Human–computer interaction #Leverage (statistics) #Mental Health via Writing #Multi-task learning #Sentiment Analysis and Opinion Mining #Social media #Task (project management) #Topic Modeling #World Wide Web
paper · pdf · doi:10.48550/arxiv.2212.05147
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2022/12/09 · openalex created_date 2022/12/26 · openalex updated_date 2026/08/06
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.