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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2022
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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. |
format | Online Article Text |
id | pubmed-9299280 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
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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