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Social Media Intelligence for Public Health Surveillance
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Social Media Intelligence for Public Health Surveillance

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Health Communication and Informatics".

Deadline for manuscript submissions: closed (31 May 2020) | Viewed by 47530

Special Issue Editors

Prof. Rafael Berlanga

E-Mail Website
Guest Editor
Universitat Jaume I de Castellón
Interests: business intelligence; health domain; text mining; social media analysis
Dr. Antonio Jimeno-Yepes

E-Mail Website
Guest Editor
IBM Research Australia
Interests: natural language processing; social media; biomedical domain; deep learning

Special Issue Information

Dear Colleagues,

Nowadays, the impact of social networks on public health aspects is higher and higher, because of their enormous popularity and engagement power. Detecting and understanding how fake news, false rumours, and any other kind of viral information can affect public health are urgent issues that will need specific methods of social media analysis. Measuring the impact of public campaigns from authority organizations against these health threats will be especially relevant in the near future. Also, detecting social network communities that are polarized to certain public health topics will allow these organizations to better prepare their citizen awareness campaigns.  

On the other hand, the public availability of millions of user-generated messages opens up new challenges and opportunities for epidemiological and social studies related to public health surveillance. Studies about drug use in the population, like self-medication practices, pharmacovigilance, health psychology aspects such as depression, disease propagation patterns, environmental issues like pollution, and much more can be now tackled through social media analysis.

This Special Issue is aimed at original and high-quality papers about social media analysis oriented to public health surveillance in any of its facets. Papers proposing novel techniques and methods for extracting useful insight from social media data are especially welcome to this Special Issue. Moreover, papers should contribute new findings about public health that are mainly derived from social media analysis.

Potential papers of interest will include, but not be limited to, the following topics:

  1. Social media analysis
  2. Public health surveillance
  3. Bio-surveillance
  4. Pharmacovigilance
  5. Decision making
  6. Data visualization
  7. Predictive analysis
  8. Network analysis
  9. Knowledge graphs

Prof. Rafael Berlanga
Dr. Antonio Jimeno-Yepes
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 submissions that pass pre-check are 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. International Journal of Environmental Research and Public Health 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 2500 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

  • Social media analysis
  • Predictive analysis
  • Public health surveillance

Published Papers (4 papers)

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Research

23 pages, 4094 KiB  
Article
A Big Data Platform for Real Time Analysis of Signs of Depression in Social Media
by Rodrigo Martínez-Castaño, Juan C. Pichel and David E. Losada 
Int. J. Environ. Res. Public Health 2020, 17(13), 4752; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17134752 - 01 Jul 2020
Cited by 19 | Viewed by 4226
Abstract
In this paper we propose a scalable platform for real-time processing of Social Media data. The platform ingests huge amounts of contents, such as Social Media posts or comments, and can support Public Health surveillance tasks. The processing and analytical needs of multiple [...] Read more.
In this paper we propose a scalable platform for real-time processing of Social Media data. The platform ingests huge amounts of contents, such as Social Media posts or comments, and can support Public Health surveillance tasks. The processing and analytical needs of multiple screening tasks can easily be handled by incorporating user-defined execution graphs. The design is modular and supports different processing elements, such as crawlers to extract relevant contents or classifiers to categorise Social Media. We describe here an implementation of a use case built on the platform that monitors Social Media users and detects early signs of depression. Full article
(This article belongs to the Special Issue Social Media Intelligence for Public Health Surveillance)
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15 pages, 352 KiB  
Article
A New Application of Social Impact in Social Media for Overcoming Fake News in Health
by Cristina M. Pulido, Laura Ruiz-Eugenio, Gisela Redondo-Sama and Beatriz Villarejo-Carballido
Int. J. Environ. Res. Public Health 2020, 17(7), 2430; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17072430 - 03 Apr 2020
Cited by 128 | Viewed by 33080
Abstract
One of the challenges today is to face fake news (false information) in health due to its potential impact on people’s lives. This article contributes to a new application of social impact in social media (SISM) methodology. This study focuses on the social [...] Read more.
One of the challenges today is to face fake news (false information) in health due to its potential impact on people’s lives. This article contributes to a new application of social impact in social media (SISM) methodology. This study focuses on the social impact of the research to identify what type of health information is false and what type of information is evidence of the social impact shared in social media. The analysis of social media includes Reddit, Facebook, and Twitter. This analysis contributes to identifying how interactions in these forms of social media depend on the type of information shared. The results indicate that messages focused on fake health information are mostly aggressive, those based on evidence of social impact are respectful and transformative, and finally, deliberation contexts promoted in social media overcome false information about health. These results contribute to advancing knowledge in overcoming fake health-related news shared in social media. Full article
(This article belongs to the Special Issue Social Media Intelligence for Public Health Surveillance)
17 pages, 2020 KiB  
Article
Social Media Multidimensional Analysis for Intelligent Health Surveillance
by María José Aramburu, Rafael Berlanga and Indira Lanza
Int. J. Environ. Res. Public Health 2020, 17(7), 2289; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17072289 - 28 Mar 2020
Cited by 9 | Viewed by 3426
Abstract
Background: Recent work in social network analysis has shown the usefulness of analysing and predicting outcomes from user-generated data in the context of Public Health Surveillance (PHS). Most of the proposals have focused on dealing with static datasets gathered from social networks, [...] Read more.
Background: Recent work in social network analysis has shown the usefulness of analysing and predicting outcomes from user-generated data in the context of Public Health Surveillance (PHS). Most of the proposals have focused on dealing with static datasets gathered from social networks, which are processed and mined off-line. However, little work has been done on providing a general framework to analyse the highly dynamic data of social networks from a multidimensional perspective. In this paper, we claim that such a framework is crucial for including social data in PHS systems. Methods: We propose a dynamic multidimensional approach to deal with social data streams. In this approach, dynamic dimensions are continuously updated by applying unsupervised text mining methods. More specifically, we analyse the semantics and temporal patterns in posts for identifying relevant events, topics and users. We also define quality metrics to detect relevant user profiles. In this way, the incoming data can be further filtered to cope with the goals of PHS systems. Results: We have evaluated our approach over a long-term stream of Twitter. We show how the proposed quality metrics allow us to filter out the users that are out-of-domain as well as those with low quality in their messages. We also explain how specific user profiles can be identified through their descriptions. Finally, we illustrate how the proposed multidimensional model can be used to identify main events and topics, as well as to analyse their audience and impact. Conclusions: The results show that the proposed dynamic multidimensional model is able to identify relevant events and topics and analyse them from different perspectives, which is especially useful for PHS systems. Full article
(This article belongs to the Special Issue Social Media Intelligence for Public Health Surveillance)
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12 pages, 4776 KiB  
Article
Information Management in Healthcare and Environment: Towards an Automatic System for Fake News Detection
by Pablo Lara-Navarra, Hervé Falciani, Enrique A. Sánchez-Pérez and Antonia Ferrer-Sapena
Int. J. Environ. Res. Public Health 2020, 17(3), 1066; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17031066 - 08 Feb 2020
Cited by 26 | Viewed by 5998
Abstract
Comments and information appearing on the internet and on different social media sway opinion concerning potential remedies for diagnosing and curing diseases. In many cases, this has an impact on citizens’ health and affects medical professionals, who find themselves having to defend their [...] Read more.
Comments and information appearing on the internet and on different social media sway opinion concerning potential remedies for diagnosing and curing diseases. In many cases, this has an impact on citizens’ health and affects medical professionals, who find themselves having to defend their diagnoses as well as the treatments they propose against ill-informed patients. The propagation of these opinions follows the same pattern as the dissemination of fake news about other important topics, such as the environment, via social media networks, which we use as a testing ground for checking our procedure. In this article, we present an algorithm to analyse the behaviour of users of Twitter, the most important social network with respect to this issue, as well as a dynamic knowledge graph construction method based on information gathered from Twitter and other open data sources such as web pages. To show our methodology, we present a concrete example of how the associated graph structure of the tweets related to World Environment Day 2019 is used to develop a heuristic analysis of the validity of the information. The proposed analytical scheme is based on the interaction between the computer tool—a database implemented with Neo4j—and the analyst, who must ask the right questions to the tool, allowing to follow the line of any doubtful data. We also show how this method can be used. We also present some methodological guidelines on how our system could allow, in the future, an automation of the procedures for the construction of an autonomous algorithm for the detection of false news on the internet related to health. Full article
(This article belongs to the Special Issue Social Media Intelligence for Public Health Surveillance)
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