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Crowdsourcing sensitive VGI: Constructing the hate incident reporting system
Gaps in hate crime and hate incident data are a major roadblock in increasing our understanding of the rising phenomenon of hate in the United States. In this paper, we reflect on the development of our geographically-integrated mobile application (the Hate Incident Reporting System) as an attempt t...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Authors. Published by Elsevier Ltd.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7442566/ http://dx.doi.org/10.1016/j.diggeo.2020.100003 |
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author | Nicolosi, Emily Medina, Richard Riley, Collin McNeally, Phoebe |
author_facet | Nicolosi, Emily Medina, Richard Riley, Collin McNeally, Phoebe |
author_sort | Nicolosi, Emily |
collection | PubMed |
description | Gaps in hate crime and hate incident data are a major roadblock in increasing our understanding of the rising phenomenon of hate in the United States. In this paper, we reflect on the development of our geographically-integrated mobile application (the Hate Incident Reporting System) as an attempt to help close the gap in hate incident data. More broadly, we provide conceptual and methodological insights for working with sensitive Volunteered Geographic Information (VGI) like hate incidents. We identify four key areas of attention in the process of developing digital tools for collecting sensitive VGI: i) participant motivation ii) data management and public research communication iii) accessibility iv) handling of geographic information and v) partnership with existing stakeholders. These factors are critical in the process of working with sensitive geographic data in an ethical fashion and ensuring maximum data reliability. Throughout each of these areas, the role of the ethical researcher stretches beyond academic research to accountability beyond academic research to accountability to participants in the form of tangible benefits. |
format | Online Article Text |
id | pubmed-7442566 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | The Authors. Published by Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-74425662020-08-24 Crowdsourcing sensitive VGI: Constructing the hate incident reporting system Nicolosi, Emily Medina, Richard Riley, Collin McNeally, Phoebe Digital Geography and Society Article Gaps in hate crime and hate incident data are a major roadblock in increasing our understanding of the rising phenomenon of hate in the United States. In this paper, we reflect on the development of our geographically-integrated mobile application (the Hate Incident Reporting System) as an attempt to help close the gap in hate incident data. More broadly, we provide conceptual and methodological insights for working with sensitive Volunteered Geographic Information (VGI) like hate incidents. We identify four key areas of attention in the process of developing digital tools for collecting sensitive VGI: i) participant motivation ii) data management and public research communication iii) accessibility iv) handling of geographic information and v) partnership with existing stakeholders. These factors are critical in the process of working with sensitive geographic data in an ethical fashion and ensuring maximum data reliability. Throughout each of these areas, the role of the ethical researcher stretches beyond academic research to accountability beyond academic research to accountability to participants in the form of tangible benefits. The Authors. Published by Elsevier Ltd. 2020 2020-08-22 /pmc/articles/PMC7442566/ http://dx.doi.org/10.1016/j.diggeo.2020.100003 Text en © 2020 The Authors Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Nicolosi, Emily Medina, Richard Riley, Collin McNeally, Phoebe Crowdsourcing sensitive VGI: Constructing the hate incident reporting system |
title | Crowdsourcing sensitive VGI: Constructing the hate incident reporting system |
title_full | Crowdsourcing sensitive VGI: Constructing the hate incident reporting system |
title_fullStr | Crowdsourcing sensitive VGI: Constructing the hate incident reporting system |
title_full_unstemmed | Crowdsourcing sensitive VGI: Constructing the hate incident reporting system |
title_short | Crowdsourcing sensitive VGI: Constructing the hate incident reporting system |
title_sort | crowdsourcing sensitive vgi: constructing the hate incident reporting system |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7442566/ http://dx.doi.org/10.1016/j.diggeo.2020.100003 |
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