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Identifying the Risk Factors of Allergic Rhinitis Based on Zhihu Comment Data Using a Topic-Enhanced Word-Embedding

Dongxiao Gu¹, Qin Wang¹, Yidong Chai¹

¹School of Management, Hefei University of Technology, Hefei, China.

Journal of Medical Internet Research

|February 22, 2024

Summary

This summary is machine-generated.

Social media comments reveal key allergic rhinitis (AR) risk factors like season, region, and mites. This analysis aids in understanding triggers and developing management strategies for AR.

Keywords:

chronic disease management disease risk factor identification social media platforms text mining topic-enhanced word embedding

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Area of Science:

Computational linguistics
Public health informatics
Allergy research

Background:

Allergic rhinitis (AR) is a chronic condition influenced by daily environmental exposures.
Social media platforms facilitate widespread sharing of personal health experiences and information.

Purpose of the Study:

To develop an intelligent method (TopicS-ClusterREV) for identifying AR risk factors from social media comments.
To categorize identified risk factors and understand their mechanisms in triggering AR.

Main Methods:

Crawled 9628 posts and 33,747 comments related to allergic rhinitis from Zhihu (May 2012-May 2022).
Utilized an improved Skip-gram model for topic-enhanced word vector representations (TopicS).
Employed a risk factor classifier and cluster analysis to categorize and analyze triggers.

Main Results:

The TopicS-ClusterREV classifier achieved 96.1% accuracy and 96.3% recall in identifying AR risk factors.
Identified and categorized 28 distinct risk factors, with season, region, and mites being most prevalent.
Provided insights into mechanisms, such as seasonal changes disrupting the immune system.

Conclusions:

The developed approach effectively extracts AR risk factors from large social media datasets.
The identified risk factors and their triggers offer practical guidance for individuals to reduce AR.
Findings can inform the development of targeted AR management and intervention strategies.