International Journal of Environmental Research and Public Health

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Dr. Juyoung Song

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Guest Editor

Crimial Justice, Pennsylvania State University, Schuylkill, PA 17972, USA
Interests: big data analysis; machine learning; juvenile delinquency; bullying and cyberbullying

Special Issue Information

Dear Colleagues,

Big data is one element that has consistently helped to achieve public health goals through its ability to deliver to practitioners a variety of structured or unstructured data not previously possible. Big data has enabled more widespread and specific research and trials of stratifying and segmenting populations at risk for a variety of health problems. The challenges and opportunities for big data in public health in modern society are more complex and diverse than in the past. There are many ways to predict and analyze the phenomena. The current era of the fourth industrial revolution is experiencing innovative changes as cutting-edge information and communications technology are incorporated into all areas of the economy and society, for example, big data, artificial intelligence (AI), the Internet of Things, and mobile technology. Researchers play a very important role in this process. They create or assemble high-quality data that can be used to train machine-learning systems, find machine-learning algorithms that are suitable for the data, and perform modeling. This Special Issue will introduce the challenges and opportunities in big data in public health.

Dr. Juyoung Song
Guest Editor

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Keywords

big data
public health
machine learning
social big data
artificial intelligence

Published Papers (2 papers)

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Research

11 pages, 808 KiB

Open AccessArticle

Exploring Future Signals of COVID-19 and Response to Information Diffusion Using Social Media Big Data

by Juyoung Song, Dal-Lae Jin, Tae Min Song and Sang Ho Lee

Int. J. Environ. Res. Public Health 2023, 20(9), 5753; https://doi.org/10.3390/ijerph20095753 - 8 May 2023

Cited by 2 | Viewed by 1456

Abstract

COVID-19 is a respiratory infectious disease that first reported in Wuhan, China, in December 2019. With COVID-19 spreading to patients worldwide, the WHO declared it a pandemic on 11 March 2020. This study collected 1,746,347 tweets from the Korean-language version of Twitter between February and May 2020 to explore future signals of COVID-19 and present response strategies for information diffusion. To explore future signals, we analyzed the term frequency and document frequency of key factors occurring in the tweets, analyzing the degree of visibility and degree of diffusion. Depression, digestive symptoms, inspection, diagnosis kits, and stay home obesity had high frequencies. The increase in the degree of visibility was higher than the median value, indicating that the signal became stronger with time. The degree of visibility of the mean word frequency was high for disinfectant, healthcare, and mask. However, the increase in the degree of visibility was lower than the median value, indicating that the signal grew weaker with time. Infodemic had a higher degree of diffusion mean word frequency. However, the mean degree of diffusion increase rate was lower than the median value, indicating that the signal grew weaker over time. As the general flow of signal progression is latent signal → weak signal → strong signal → strong signal with lower increase rate, it is necessary to obtain active response strategies for stay home, inspection, obesity, digestive symptoms, online shopping, and asymptomatic. Full article

(This article belongs to the Special Issue Big Data in Public Health: Challenges and Opportunities)

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11 pages, 2344 KiB

Open AccessArticle

Analysis of Caregiver Burden Expressed in Social Media Discussions

by Catherine C. Shoults, Michael W. Rutherford, Aaron S. Kemp, Merideth A. Addicott, Aliza Brown, Carolyn J. Greene, Corey J. Hayes, Jennifer M. Gan, Linda J. Larson-Prior and Jonathan P. Bona

Int. J. Environ. Res. Public Health 2023, 20(3), 1933; https://doi.org/10.3390/ijerph20031933 - 20 Jan 2023

Cited by 2 | Viewed by 2255

Abstract

Almost 40% of US adults provide informal caregiving, yet research gaps remain around what burdens affect informal caregivers. This study uses a novel social media site, Reddit, to mine and better understand what online communities focus on as their caregiving burdens. These forums were accessed using an application programming interface, a machine learning classifier was developed to remove low information posts, and topic modeling was applied to the corpus. An expert panel summarized the forums’ themes into ten categories. The largest theme extracted from Reddit’s forums discussed the personal emotional toll of being a caregiver. This was followed by logistic issues while caregiving and caring for parents who have cancer. Smaller themes included approaches to end-of-life care, physical equipment needs when caregiving, and the use of wearables or technology to help monitor care recipients. The platform often discusses caregiving for parents which may reflect the age of Reddit’s users. This study confirms that Reddit forums are used for caregivers to discuss the burdens associated with their role and the types of stress that can result from informal caregiving. Full article

(This article belongs to the Special Issue Big Data in Public Health: Challenges and Opportunities)

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