Mo, Chen, Yin, Jingjing, Fung, Isaac Chun Hai et al. (1 more author) (2021) Aggregating twitter text through generalized linear regression models for tweet popularity prediction and automatic topic classification. European Journal of Investigation in Health, Psychology and Education. 1554. ISSN 2254-9625
Abstract
Social media platforms have become accessible resources for health data analysis. How-ever, the advanced computational techniques involved in big data text mining and analysis are chal-lenging for public health data analysts to apply. This study proposes and explores the feasibility of a novel yet straightforward method by regressing the outcome of interest on the aggregated influence scores for association and/or classification analyses based on generalized linear models. The method reduces the document term matrix by transforming text data into a continuous summary score, thereby reducing the data dimension substantially and easing the data sparsity issue of the term matrix. To illustrate the proposed method in detailed steps, we used three Twitter datasets on various topics: autism spectrum disorder, influenza, and violence against women. We found that our results were generally consistent with the critical factors associated with the specific public health topic in the existing literature. The proposed method could also classify tweets into different topic groups appropriately with consistent performance compared with existing text mining methods for automatic classification based on tweet contents.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | Publisher Copyright: © 2021 by the authors. |
Keywords: | Document term matrix,Hurdle model,Odds ratio,Regression,Relative risk,Social network,Text data |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Electronic Engineering (York) |
Depositing User: | Pure (York) |
Date Deposited: | 01 Jul 2022 09:50 |
Last Modified: | 07 Dec 2024 00:23 |
Published Version: | https://doi.org/10.3390/ejihpe11040109 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.3390/ejihpe11040109 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:188628 |