Faces in the crowd: Twitter as alternative to protest surveys

Who goes to protests? To answer this question, existing research has relied either on retrospective surveys of populations or in-protest surveys of participants. Both techniques are prohibitively costly and face logistical and methodological constraints. In this article, we investigate the possibili...

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Autores principales: Christopher Barrie, Arun Frey
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Lenguaje:EN
Publicado: Public Library of Science (PLoS) 2021
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Acceso en línea:https://doaj.org/article/000b6f03a7da47c899e72dab66d10383
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spelling oai:doaj.org-article:000b6f03a7da47c899e72dab66d103832021-11-25T06:19:43ZFaces in the crowd: Twitter as alternative to protest surveys1932-6203https://doaj.org/article/000b6f03a7da47c899e72dab66d103832021-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8601430/?tool=EBIhttps://doaj.org/toc/1932-6203Who goes to protests? To answer this question, existing research has relied either on retrospective surveys of populations or in-protest surveys of participants. Both techniques are prohibitively costly and face logistical and methodological constraints. In this article, we investigate the possibility of surveying protests using Twitter. We propose two techniques for sampling protestors on the ground from digital traces and estimate the demographic and ideological composition of ten protestor crowds using multidimensional scaling and machine-learning techniques. We test the accuracy of our estimates by comparing to two in-protest surveys from the 2017 Women’s March in Washington, D.C. Results show that our Twitter sampling techniques are superior to hashtag sampling alone. They also approximate the ideology and gender distributions derived from on-the-ground surveys, albeit with some bias, but fail to retrieve accurate age group estimates. We conclude that online samples are yet unable to provide reliable representative samples of offline protest.Christopher BarrieArun FreyPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 11 (2021)
institution DOAJ
collection DOAJ
language EN
topic Medicine
R
Science
Q
spellingShingle Medicine
R
Science
Q
Christopher Barrie
Arun Frey
Faces in the crowd: Twitter as alternative to protest surveys
description Who goes to protests? To answer this question, existing research has relied either on retrospective surveys of populations or in-protest surveys of participants. Both techniques are prohibitively costly and face logistical and methodological constraints. In this article, we investigate the possibility of surveying protests using Twitter. We propose two techniques for sampling protestors on the ground from digital traces and estimate the demographic and ideological composition of ten protestor crowds using multidimensional scaling and machine-learning techniques. We test the accuracy of our estimates by comparing to two in-protest surveys from the 2017 Women’s March in Washington, D.C. Results show that our Twitter sampling techniques are superior to hashtag sampling alone. They also approximate the ideology and gender distributions derived from on-the-ground surveys, albeit with some bias, but fail to retrieve accurate age group estimates. We conclude that online samples are yet unable to provide reliable representative samples of offline protest.
format article
author Christopher Barrie
Arun Frey
author_facet Christopher Barrie
Arun Frey
author_sort Christopher Barrie
title Faces in the crowd: Twitter as alternative to protest surveys
title_short Faces in the crowd: Twitter as alternative to protest surveys
title_full Faces in the crowd: Twitter as alternative to protest surveys
title_fullStr Faces in the crowd: Twitter as alternative to protest surveys
title_full_unstemmed Faces in the crowd: Twitter as alternative to protest surveys
title_sort faces in the crowd: twitter as alternative to protest surveys
publisher Public Library of Science (PLoS)
publishDate 2021
url https://doaj.org/article/000b6f03a7da47c899e72dab66d10383
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AT arunfrey facesinthecrowdtwitterasalternativetoprotestsurveys
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