Tracking Census Online Self-Completion Using Twitter Posts

IF 3 2区 社会学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Mao Li, Frederick Conrad
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引用次数: 0

Abstract

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.
利用 Twitter 帖子跟踪人口普查在线自我填写情况
从 2020 年美国人口普查的数据收集开始,官方用户和名人用户就在推特上大肆宣扬人口普查中每个人都被计算在内的重要性,并敦促关注者填写调查问卷(即所谓的社交媒体活动)。根据人口普查局的数据,本研究区分了不同的推特用户群体原型,并调查了他们对 2020 年人口普查(在线)自我填写率可能产生的影响。使用网络分析方法 "社区检测 "和聚类算法 "潜在德里希特分配"(LDA),确定了三个原型用户群:"官方政府机构"、"人口普查倡导者 "和 "人口普查怀疑者"。人口普查怀疑论者 "原型用户的动机是某个有影响力的人在推特上发布的事件(例如,"国会中的共和党人表示人口普查不能花额外的时间来统计")。在研究期间,这一群体成为最大的群体。原型人口普查拥护者更多受到官方推文的激励,比原型人口普查怀疑者更活跃。官方政府机构用户群是三个用户群中最小的,但他们的信息--主要是促进人口普查的完成--似乎被人口普查倡导者,尤其是名人和政客所放大。我们发现,"人口普查倡导者 "用户群(而非其他两个用户群)的每日规模可以预测推文发布后五天内的 2020 年人口普查在线自我完成率。这一发现表明,人口普查社交媒体活动在促进填写方面取得了成功,这显然要归功于人口普查倡导者用户的帮助,他们鼓励人们填写人口普查并放大了官方推文。这一发现表明,对于像十年一次的人口普查这样重要的国家项目,社交媒体活动可以对公众行为产生积极影响。
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来源期刊
Social Science Computer Review
Social Science Computer Review 社会科学-计算机:跨学科应用
CiteScore
9.00
自引率
4.90%
发文量
95
审稿时长
>12 weeks
期刊介绍: Unique Scope Social Science Computer Review is an interdisciplinary journal covering social science instructional and research applications of computing, as well as societal impacts of informational technology. Topics included: artificial intelligence, business, computational social science theory, computer-assisted survey research, computer-based qualitative analysis, computer simulation, economic modeling, electronic modeling, electronic publishing, geographic information systems, instrumentation and research tools, public administration, social impacts of computing and telecommunications, software evaluation, world-wide web resources for social scientists. Interdisciplinary Nature Because the Uses and impacts of computing are interdisciplinary, so is Social Science Computer Review. The journal is of direct relevance to scholars and scientists in a wide variety of disciplines. In its pages you''ll find work in the following areas: sociology, anthropology, political science, economics, psychology, computer literacy, computer applications, and methodology.
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