Special Issue "Social Media Data Analysis for Public Health: Methods, Techniques and Real World Cases"

A special issue of Healthcare (ISSN 2227-9032). This special issue belongs to the section "Health Informatics and Big Data".

Deadline for manuscript submissions: 30 September 2021.

Special Issue Editors

Dr. Alejandro Rodríguez González
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Guest Editor
Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid, Spain
Interests: medical informatics; knowledge acquisition; disease networks; social media
Dr. José Alberto Benítez Andrades
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Guest Editor
Salud Bienestar Ingeniería y Sostenibilidad Sociosanitaria (SALBIS) Research Group, Department of Electric, Systems and Automatics Engineering, University of León, Campus of Vegazana s/n, León, 24071 León, Spain
Interests: knowledge engineering; ontologies; artificial intelligence; machine learning; eHealth; public health
Special Issues and Collections in MDPI journals
Dr. Jose María Alvarez Rodríguez
Website
Guest Editor
Department of Computer Science and Engineering, Carlos III University of Madrid, Madrid, Spain
Interests: knowledge engineering; complex systems; service-oriented computing; interoperability; social network analysis

Special Issue Information

Dear Colleagues,

The amount of information available online is increasing every day, and new tools, architectures, and approaches for dealing with such a large amount of data are necessary. Moreover, one of the areas where the amount of information is growing rapidly is in social networks, where social media content is being produced at an extreme speed. In these social media forums, the users can talk about anything, including topics related to medicine and healthcare. We require new approaches to dealing with this kind of information to be transformed into actionable knowledge. In a connected world, the information provided in social media can help to determine new public health policies and actions.

This Special Issue aims to bring together works focused on the application of real-world use cases, scenarios, and approaches that take advantage of the creation and consumption of health-related information in social media for the public health sector.

Dr. Alejandro Rodríguez González
Dr. José Alberto Benítez Andrades
Dr. Jose María Alvarez Rodríguez
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Healthcare is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • health monitoring and surveillance using social networks and media
  • data analysis over social networks and media
  • public health policies and social networks and media
  • knowledge extraction and representation of health-related topics in social media
  • ontology-based healthcare systems
  • deep learning in healthcare
  • machine learning in healthcare
  • collective intelligence in social networks and media.

Published Papers (4 papers)

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Research

Open AccessArticle
Google Trends on Obesity, Smoking and Alcoholism: Global and Country-Specific Interest
Healthcare 2021, 9(2), 190; https://doi.org/10.3390/healthcare9020190 - 09 Feb 2021
Abstract
Unhealthy habits or lifestyles, such as obesity, smoking, and alcohol consumption, are involved in the development of non-communicable diseases. The aim of this study was to analyze different communities’ interest in seeking obesity, smoking, and alcohol-related terms through relative search volumes (RSVs) of [...] Read more.
Unhealthy habits or lifestyles, such as obesity, smoking, and alcohol consumption, are involved in the development of non-communicable diseases. The aim of this study was to analyze different communities’ interest in seeking obesity, smoking, and alcohol-related terms through relative search volumes (RSVs) of Google Trends (GT). Internet search query data on obesity, smoking, and alcohol-related terms were obtained from GT from the period between 2010 and 2020. Comparisons and correlations between different topics were calculated considering both global searches and English-, Spanish-, and Italian-speaking areas. Globally, the RSVs for obesity and alcohol-related terms were similar (mean RSVs: 76% and 77%), but they were lower for smoking (65%). High RSVs were found in winter for obesity and smoking-related terms. Worldwide, a negative correlation was found between alcohol and smoking terms (r = −0.72, p < 0.01). In Italy, the correlation was positive (r = 0.58). The correlation between obesity and alcohol was positive in all the cases considered. The interest of global citizens in obesity, smoking, and alcohol was high. The RSVs for obesity were globally higher and correlated with alcohol. Alcohol and smoking terms were related depending on the area considered. Full article
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Open AccessArticle
Combining Public Opinion Dissemination with Polarization Process Considering Individual Heterogeneity
Healthcare 2021, 9(2), 176; https://doi.org/10.3390/healthcare9020176 - 07 Feb 2021
Abstract
The wide dissemination of false information and the frequent occurrence of extreme speeches on online social platforms have become increasingly prominent, which impact on the harmony and stability of society. In order to solve the problems in the dissemination and polarization of public [...] Read more.
The wide dissemination of false information and the frequent occurrence of extreme speeches on online social platforms have become increasingly prominent, which impact on the harmony and stability of society. In order to solve the problems in the dissemination and polarization of public opinion over online social platforms, it is necessary to conduct in-depth research on the formation mechanism of the dissemination and polarization of public opinion. This article appends individual communicating willingness and forgetting effects to the Susceptible-Exposed-Infected-Recovered (SEIR) model to describe individual state transitions; secondly, it introduces three heterogeneous factors describing the characteristics of individual differences in the Jager-Amblard (J-A) model, namely: Individual conformity, individual conservative degree, and inter-individual relationship strength in order to reflect the different roles of individual heterogeneity in the opinions interaction; thirdly, it integrates the improved SEIR model and J-A model to construct the SEIR-JA model to study the formation mechanism of public opinion dissemination and polarization. Transmission parameters and polarization parameters are simulated and analyzed. Finally, a public opinion event from the pricing of China’s self-developed COVID-19 vaccine are used, and related Weibo comment data about this event are also collected so as to verify the rationality and effectiveness of the proposed model. Full article
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Open AccessArticle
Factors Affecting Social Media Users’ Emotions Regarding Food Safety Issues: Content Analysis of a Debate among Chinese Weibo Users on Genetically Modified Food Security
Healthcare 2021, 9(2), 113; https://doi.org/10.3390/healthcare9020113 - 21 Jan 2021
Abstract
Social media is gradually building an online information environment regarding health. This environment is filled with many types of users’ emotions regarding food safety, especially negative emotions that can easily cause panic or anger among the population. However, the mechanisms of how it [...] Read more.
Social media is gradually building an online information environment regarding health. This environment is filled with many types of users’ emotions regarding food safety, especially negative emotions that can easily cause panic or anger among the population. However, the mechanisms of how it affects users’ emotions have not been fully studied. Therefore, from the perspective of communication and social psychology, this study uses the content analysis method to analyze factors affecting social media users’ emotions regarding food safety issues. In total, 371 tweet samples of genetically modified food security in Sina Weibo (similar to Twitter) were encoded, measured, and analyzed. The major findings are as follows: (1) Tweet account type, tweet topic, and emotion object were all significantly related to emotion type. Tweet depth and objectivity were both positively affected by emotion type, and objectivity had a greater impact. (2) Account type, tweet topic, and emotion object were all significantly related to emotion intensity. When the depths were the same, emotion intensity became stronger with the decrease in objectivity. (3) Account type, tweet topic, emotion object, and emotion type were all significantly related to a user’s emotion communication capacity. Tweet depth, objectivity, and user’s emotion intensity were positively correlated with emotion communication capacity. Positive emotions had stronger communication capacities than negative ones, which is not consistent with previous studies. These findings help us to understand both theoretically and practically the changes and dissemination of user’s emotions in a food safety and health information environment. Full article
Open AccessArticle
Evaluation of Country Dietary Habits Using Machine Learning Techniques in Relation to Deaths from COVID-19
Healthcare 2020, 8(4), 371; https://doi.org/10.3390/healthcare8040371 - 29 Sep 2020
Cited by 1
Abstract
COVID-19 disease has affected almost every country in the world. The large number of infected people and the different mortality rates between countries has given rise to many hypotheses about the key points that make the virus so lethal in some places. In [...] Read more.
COVID-19 disease has affected almost every country in the world. The large number of infected people and the different mortality rates between countries has given rise to many hypotheses about the key points that make the virus so lethal in some places. In this study, the eating habits of 170 countries were evaluated in order to find correlations between these habits and mortality rates caused by COVID-19 using machine learning techniques that group the countries together according to the different distribution of fat, energy, and protein across 23 different types of food, as well as the amount ingested in kilograms. Results shown how obesity and the high consumption of fats appear in countries with the highest death rates, whereas countries with a lower rate have a higher level of cereal consumption accompanied by a lower total average intake of kilocalories. Full article
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