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Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

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Aggregating Twitter Text through Generalized Linear Regression Models for Tweet Popularity Prediction and Automatic

Chen Mo¹, Jingjing Yin¹, Isaac Chun-Hai Fung¹

¹Department of Biostatistics, Epidemiology and Environmental Health Sciences, Jiann-Ping Hsu College Public Health, Georgia Southern University, Statesboro, GA 30458, USA.

European Journal of Investigation in Health, Psychology and Education

|December 23, 2021

Summary

This study introduces a simple method for analyzing public health data from social media. It simplifies big data text mining, making health data analysis more accessible for public health professionals.

Keywords:

document term matrix hurdle model odds ratio regression relative risk social network text data

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Area of Science:

Public Health
Computational Linguistics
Data Science

Background:

Social media platforms are rich sources of health data.
Advanced big data text mining techniques pose challenges for public health analysts.
Existing methods for analyzing social media health data are complex.

Purpose of the Study:

To propose and explore a novel, straightforward method for health data analysis using social media.
To simplify the application of computational techniques for public health data analysts.
To reduce data dimensionality and address sparsity issues in text mining.

Main Methods:

Regressing outcomes on aggregated influence scores using generalized linear models.
Transforming text data into a continuous summary score to reduce the document term matrix.
Applying the method to three Twitter datasets: autism spectrum disorder, influenza, and violence against women.

Main Results:

The proposed method's results align with established literature on public health topics.
The method effectively reduces data dimensionality and mitigates the term matrix sparsity issue.
The approach demonstrated appropriate classification of tweets into topic groups.

Conclusions:

The novel method offers a feasible and simplified approach to analyzing public health data from social media.
This technique enhances accessibility of big data text mining for public health professionals.
The method shows consistent performance in both association analyses and automatic tweet classification.