Assessing the Severity of Health States based on Social Media Posts

TL;DR

This paper presents a multiview learning framework that leverages various natural language understanding aspects to assess health severity from social media posts, aiding health professionals in prioritizing patient concerns.

Contribution

It introduces a novel multiview model combining textual and contextual features like sentiment and emotions for health state severity assessment in online communities.

Findings

Effective in identifying health severity across different diseases

Utilizes diverse NLU features for improved accuracy

Assists health professionals in prioritizing patient posts

Abstract

The unprecedented growth of Internet users has resulted in an abundance of unstructured information on social media including health forums, where patients request health-related information or opinions from other users. Previous studies have shown that online peer support has limited effectiveness without expert intervention. Therefore, a system capable of assessing the severity of health state from the patients' social media posts can help health professionals (HP) in prioritizing the user's post. In this study, we inspect the efficacy of different aspects of Natural Language Understanding (NLU) to identify the severity of the user's health state in relation to two perspectives(tasks) (a) Medical Condition (i.e., Recover, Exist, Deteriorate, Other) and (b) Medication (i.e., Effective, Ineffective, Serious Adverse Effect, Other) in online health communities. We propose a multiview learning framework that models both the textual content as well as contextual-information to assess the severity of the user's health state. Specifically, our model utilizes the NLU views such as sentiment, emotions, personality, and use of figurative language to extract the contextual information. The diverse NLU views demonstrate its effectiveness on both the tasks and as well as on the individual disease to assess a user's health.