Building Political Hashtag Communities: A Multiplex Network Analysis of U.S. Senators on Twitter during the 2022 Midterm Elections

IF 1.9 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Computation Pub Date : 2023-12-01 DOI:10.3390/computation11120238
Yunus Emre Orhan, Harun Pirim, Yusuf Akbulut
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Abstract

This study examines how U.S. senators strategically used hashtags to create political communities on Twitter during the 2022 Midterm Elections. We propose a way to model topic-based implicit interactions among Twitter users and introduce the concept of Building Political Hashtag Communities (BPHC). Using multiplex network analysis, we provide a comprehensive view of elites’ behavior. Through AI-driven topic modeling on real-world data, we observe that, at a general level, Democrats heavily rely on BPHC. Yet, when disaggregating the network across layers, this trend does not uniformly persist. Specifically, while Republicans engage more intensively in BPHC discussions related to immigration, Democrats heavily rely on BPHC in topics related to identity and women. However, only a select group of Democratic actors engage in BPHC for topics on labor and the environment—domains where Republicans scarcely, if at all, participate in BPHC efforts. This research contributes to the understanding of digital political communication, offering new insights into echo chamber dynamics and the role of politicians in polarization.
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建立政治标签社区:2022 年中期选举期间推特上美国参议员的多重网络分析
这项研究考察了美国参议员在2022年中期选举期间如何策略性地使用标签在推特上创建政治社区。我们提出了一种方法来模拟Twitter用户之间基于主题的隐式交互,并引入了构建政治标签社区(BPHC)的概念。通过多元网络分析,我们提供了精英行为的全面视图。通过对真实世界数据进行人工智能驱动的主题建模,我们观察到,在一般层面上,民主党严重依赖BPHC。然而,当跨层分解网络时,这种趋势并不一致地持续下去。具体来说,共和党人更多地参与与移民有关的BPHC讨论,而民主党人在与身份和妇女有关的话题上严重依赖BPHC。然而,只有一小部分民主党人在劳工和环境领域参与BPHC,而共和党人几乎没有参与BPHC的工作。这项研究有助于理解数字政治传播,为回音室动力学和政治家在两极分化中的作用提供了新的见解。
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来源期刊
Computation
Computation Mathematics-Applied Mathematics
CiteScore
3.50
自引率
4.50%
发文量
201
审稿时长
8 weeks
期刊介绍: Computation a journal of computational science and engineering. Topics: computational biology, including, but not limited to: bioinformatics mathematical modeling, simulation and prediction of nucleic acid (DNA/RNA) and protein sequences, structure and functions mathematical modeling of pathways and genetic interactions neuroscience computation including neural modeling, brain theory and neural networks computational chemistry, including, but not limited to: new theories and methodology including their applications in molecular dynamics computation of electronic structure density functional theory designing and characterization of materials with computation method computation in engineering, including, but not limited to: new theories, methodology and the application of computational fluid dynamics (CFD) optimisation techniques and/or application of optimisation to multidisciplinary systems system identification and reduced order modelling of engineering systems parallel algorithms and high performance computing in engineering.
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