An Empirical Study on Prediction of Population Health through Social Media.

Clicks: 393
ID: 44832
2019
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #4 of 30 articles by views in journal of biomedical informatics

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Public health measurement is important for government administration as it provides indicators and implications to public healthcare strategies. The measurement of health status has been traditionally conducted via surveys in the forms of pre-designed questionnaires to collect responses from targeted participants. Apart from benefits, traditional approach is costly, time-consuming, and not scalable. These limitations make a major obstacle to policy makers to develop up-to-date healthcare programs. This paper studies the use of health-related information conveyed in user-generated content from social media for prediction of health outcomes at population level. Specifically, we investigate linguistic features for analysing textual data. We propose the use of visual features learnt from deep neural networks for understanding visual data. We introduce collective social capital information from location-based social media data. We conducted extensive experiments on large-scale datasets collected from two online social networks: Foursquare and Flickr, against the task of prediction of the U.S. county health indices. Experimental results showed that visual and collective social capital data achieved comparable prediction performance and outperformed textual information. These promising results also suggest the potential of social media for health analysis at population scales.
Reference Key
nguyen2019anjournal Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Nguyen, Hung;Nguyen, Thin;Nguyen, Duc Thanh;
Journal journal of biomedical informatics
Year 2019
DOI
S1532-0464(19)30196-0
URL
Keywords
social media public health healthcare

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.