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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Public Library of Science (PLoS)
2021
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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) |
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Medicine R Science Q Christopher Barrie Arun Frey Faces in the crowd: Twitter as alternative to protest surveys |
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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 |
work_keys_str_mv |
AT christopherbarrie facesinthecrowdtwitterasalternativetoprotestsurveys AT arunfrey facesinthecrowdtwitterasalternativetoprotestsurveys |
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