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How Twitter data sampling biases U.S. voter behavior characterizations

Online social media are key platforms for the public to discuss political issues. As a result, researchers have used data from these platforms to analyze public opinions and forecast election results. The literature has shown that due to inauthentic actors such as malicious social bots and trolls, n...

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Detalles Bibliográficos
Autores principales: Yang, Kai-Cheng, Hui, Pik-Mai, Menczer, Filippo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9299280/
https://www.ncbi.nlm.nih.gov/pubmed/35875635
http://dx.doi.org/10.7717/peerj-cs.1025
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author Yang, Kai-Cheng
Hui, Pik-Mai
Menczer, Filippo
author_facet Yang, Kai-Cheng
Hui, Pik-Mai
Menczer, Filippo
author_sort Yang, Kai-Cheng
collection PubMed
description Online social media are key platforms for the public to discuss political issues. As a result, researchers have used data from these platforms to analyze public opinions and forecast election results. The literature has shown that due to inauthentic actors such as malicious social bots and trolls, not every message is a genuine expression from a legitimate user. However, the prevalence of inauthentic activities in social data streams is still unclear, making it difficult to gauge biases of analyses based on such data. In this article, we aim to close this gap using Twitter data from the 2018 U.S. midterm elections. We propose an efficient and low-cost method to identify voters on Twitter and systematically compare their behaviors with different random samples of accounts. We find that some accounts flood the public data stream with political content, drowning the voice of the majority of voters. As a result, these hyperactive accounts are over-represented in volume samples. Hyperactive accounts are more likely to exhibit various suspicious behaviors and to share low-credibility information compared to likely voters. Our work provides insights into biased voter characterizations when using social media data to analyze political issues.
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spelling pubmed-92992802022-07-21 How Twitter data sampling biases U.S. voter behavior characterizations Yang, Kai-Cheng Hui, Pik-Mai Menczer, Filippo PeerJ Comput Sci Data Science Online social media are key platforms for the public to discuss political issues. As a result, researchers have used data from these platforms to analyze public opinions and forecast election results. The literature has shown that due to inauthentic actors such as malicious social bots and trolls, not every message is a genuine expression from a legitimate user. However, the prevalence of inauthentic activities in social data streams is still unclear, making it difficult to gauge biases of analyses based on such data. In this article, we aim to close this gap using Twitter data from the 2018 U.S. midterm elections. We propose an efficient and low-cost method to identify voters on Twitter and systematically compare their behaviors with different random samples of accounts. We find that some accounts flood the public data stream with political content, drowning the voice of the majority of voters. As a result, these hyperactive accounts are over-represented in volume samples. Hyperactive accounts are more likely to exhibit various suspicious behaviors and to share low-credibility information compared to likely voters. Our work provides insights into biased voter characterizations when using social media data to analyze political issues. PeerJ Inc. 2022-07-01 /pmc/articles/PMC9299280/ /pubmed/35875635 http://dx.doi.org/10.7717/peerj-cs.1025 Text en © 2022 Yang et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Data Science
Yang, Kai-Cheng
Hui, Pik-Mai
Menczer, Filippo
How Twitter data sampling biases U.S. voter behavior characterizations
title How Twitter data sampling biases U.S. voter behavior characterizations
title_full How Twitter data sampling biases U.S. voter behavior characterizations
title_fullStr How Twitter data sampling biases U.S. voter behavior characterizations
title_full_unstemmed How Twitter data sampling biases U.S. voter behavior characterizations
title_short How Twitter data sampling biases U.S. voter behavior characterizations
title_sort how twitter data sampling biases u.s. voter behavior characterizations
topic Data Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9299280/
https://www.ncbi.nlm.nih.gov/pubmed/35875635
http://dx.doi.org/10.7717/peerj-cs.1025
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