Cargando…

Are papers addressing certain diseases perceived where these diseases are prevalent? The proposal to use Twitter data as social-spatial sensors

We propose to use Twitter data as social-spatial sensors. This study deals with the question whether research papers on certain diseases are perceived by people in regions (worldwide) that are especially concerned by these diseases. Since (some) Twitter data contain location information, it is possi...

Descripción completa

Detalles Bibliográficos
Autores principales: Bornmann, Lutz, Haunschild, Robin, Patel, Vanash M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7678968/
https://www.ncbi.nlm.nih.gov/pubmed/33216816
http://dx.doi.org/10.1371/journal.pone.0242550
_version_ 1783612256029769728
author Bornmann, Lutz
Haunschild, Robin
Patel, Vanash M.
author_facet Bornmann, Lutz
Haunschild, Robin
Patel, Vanash M.
author_sort Bornmann, Lutz
collection PubMed
description We propose to use Twitter data as social-spatial sensors. This study deals with the question whether research papers on certain diseases are perceived by people in regions (worldwide) that are especially concerned by these diseases. Since (some) Twitter data contain location information, it is possible to spatially map the activity of Twitter users referring to certain papers (e.g., dealing with tuberculosis). The resulting maps reveal whether heavy activity on Twitter is correlated with large numbers of people having certain diseases. In this study, we focus on tuberculosis, human immunodeficiency virus (HIV), and malaria, since the World Health Organization ranks these diseases as the top three causes of death worldwide by a single infectious agent. The results of the social-spatial Twitter maps (and additionally performed regression models) reveal the usefulness of the proposed sensor approach. One receives an impression of how research papers on the diseases have been perceived by people in regions that are especially concerned by these diseases. Our study demonstrates a promising approach for using Twitter data for research evaluation purposes beyond simple counting of tweets.
format Online
Article
Text
id pubmed-7678968
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-76789682020-12-02 Are papers addressing certain diseases perceived where these diseases are prevalent? The proposal to use Twitter data as social-spatial sensors Bornmann, Lutz Haunschild, Robin Patel, Vanash M. PLoS One Research Article We propose to use Twitter data as social-spatial sensors. This study deals with the question whether research papers on certain diseases are perceived by people in regions (worldwide) that are especially concerned by these diseases. Since (some) Twitter data contain location information, it is possible to spatially map the activity of Twitter users referring to certain papers (e.g., dealing with tuberculosis). The resulting maps reveal whether heavy activity on Twitter is correlated with large numbers of people having certain diseases. In this study, we focus on tuberculosis, human immunodeficiency virus (HIV), and malaria, since the World Health Organization ranks these diseases as the top three causes of death worldwide by a single infectious agent. The results of the social-spatial Twitter maps (and additionally performed regression models) reveal the usefulness of the proposed sensor approach. One receives an impression of how research papers on the diseases have been perceived by people in regions that are especially concerned by these diseases. Our study demonstrates a promising approach for using Twitter data for research evaluation purposes beyond simple counting of tweets. Public Library of Science 2020-11-20 /pmc/articles/PMC7678968/ /pubmed/33216816 http://dx.doi.org/10.1371/journal.pone.0242550 Text en © 2020 Bornmann et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Bornmann, Lutz
Haunschild, Robin
Patel, Vanash M.
Are papers addressing certain diseases perceived where these diseases are prevalent? The proposal to use Twitter data as social-spatial sensors
title Are papers addressing certain diseases perceived where these diseases are prevalent? The proposal to use Twitter data as social-spatial sensors
title_full Are papers addressing certain diseases perceived where these diseases are prevalent? The proposal to use Twitter data as social-spatial sensors
title_fullStr Are papers addressing certain diseases perceived where these diseases are prevalent? The proposal to use Twitter data as social-spatial sensors
title_full_unstemmed Are papers addressing certain diseases perceived where these diseases are prevalent? The proposal to use Twitter data as social-spatial sensors
title_short Are papers addressing certain diseases perceived where these diseases are prevalent? The proposal to use Twitter data as social-spatial sensors
title_sort are papers addressing certain diseases perceived where these diseases are prevalent? the proposal to use twitter data as social-spatial sensors
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7678968/
https://www.ncbi.nlm.nih.gov/pubmed/33216816
http://dx.doi.org/10.1371/journal.pone.0242550
work_keys_str_mv AT bornmannlutz arepapersaddressingcertaindiseasesperceivedwherethesediseasesareprevalenttheproposaltousetwitterdataassocialspatialsensors
AT haunschildrobin arepapersaddressingcertaindiseasesperceivedwherethesediseasesareprevalenttheproposaltousetwitterdataassocialspatialsensors
AT patelvanashm arepapersaddressingcertaindiseasesperceivedwherethesediseasesareprevalenttheproposaltousetwitterdataassocialspatialsensors