Literature DB >> 31632599

VINCENT: A visual analytics system for investigating the online vaccine debate.

Anton Ninkov1, Kamran Sedig1.   

Abstract

This paper reports and describes VINCENT, a visual analytics system that is designed to help public health stakeholders (i.e., users) make sense of data from websites involved in the online debate about vaccines. VINCENT allows users to explore visualizations of data from a group of 37 vaccine-focused websites. These websites differ in their position on vaccines, topics of focus about vaccines, geographic location, and sentiment towards the efficacy and morality of vaccines, specific and general ones. By integrating webometrics, natural language processing of website text, data visualization, and human-data interaction, VINCENT helps users explore complex data that would be difficult to understand, and, if at all possible, to analyze without the aid of computational tools. The objectives of this paper are to explore A) the feasibility of developing a visual analytics system that integrates webometrics, natural language processing of website text, data visualization, and human-data interaction in a seamless manner; B) how a visual analytics system can help with the investigation of the online vaccine debate; and C) what needs to be taken into consideration when developing such a system. This paper demonstrates that visual analytics systems can integrate different computational techniques; that such systems can help with the exploration of online public health debates that are distributed across a set of websites; and that care should go into the design of the different components of such systems. This is an Open Access article. Authors own copyright of their articles appearing in the Journal of Public Health Informatics. Readers may copy articles without permission of the copyright owner(s), as long as the author and OJPHI are acknowledged in the copy and the copy is used for educational, not-for-profit purposes.

Entities:  

Keywords:  Data Visualization; Human-Data Interaction; Natural Language Processing; Public Health; Vaccine Debate; Visual Analytics; Webometrics

Year:  2019        PMID: 31632599      PMCID: PMC6788893          DOI: 10.5210/ojphi.v11i2.10114

Source DB:  PubMed          Journal:  Online J Public Health Inform        ISSN: 1947-2579


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