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Tracking Census Online Self-Completion Using Twitter Posts

delete2024-07-30
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AI
M
Mao Li *
F
Frederick G. Conrad
DOI:10.1177/08944393241268461delete
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摘要

摘要

En 中文
From the start of data collection for the 2020 US Census, official and celebrity users tweeted about the importance of everyone being counted in the Census and urged followers to complete the questionnaire (so-called social media campaign.) At the same time, social media posts expressing skepticism about the Census became increasingly common. This study distinguishes between different prototypical Twitter user groups and investigates their possible impact on (online) self-completion rate for the 2020 Census, according to Census Bureau data. Using a network analysis method, Community Detection, and a clustering algorithm, Latent Dirichlet Allocation (LDA), three prototypical user groups were identified: Official Government Agency, Census Advocate, and Census Skeptic. The prototypical Census Skeptic user was motivated by events about which an influential person had tweeted (e.g., Republicans in Congress signal Census cannot take extra time to count). This group became the largest one over the study period. The prototypical Census Advocate was motivated more by official tweets and was more active than the prototypical Census Skeptic. The Official Government Agency user group was the smallest of the three, but their messages-primarily promoting completion of the Census-seemed to have been amplified by Census Advocate, especially celebrities and politicians. We found that the daily size of the Census Advocate user group-but not the other two-predicted the 2020 Census online self-completion rate within five days after a tweet was posted. This finding suggests that the Census social media campaign was successful in promoting completion, apparently due to the help of Census Advocate users who encouraged people to fill out the Census and amplified official tweets. This finding demonstrates that a social media campaign can positively affect public behavior regarding an essential national project like the Decennial Census.
Keyword:
computational methods
survey
Twitter
network analysis
census 2020

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Computer Science Review
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university of michigan system
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