Polarizing Topics on Twitter in the 2022 United States Elections

被引:0
作者
Katalinic, Josip [1 ]
Dunder, Ivan [1 ]
Seljan, Sanja [1 ]
机构
[1] Univ Zagreb, Fac Humanities & Social Sci, Dept Informat & Commun Sci, Zagreb 10000, Croatia
关键词
political polarization; midterm elections; data analysis; text classification; topic modeling; sentiment analysis; machine learning; natural language processing; Twitter; SENTIMENT ANALYSIS; PARTISAN POLARIZATION; PARTY POLARIZATION;
D O I
10.3390/info14110609
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Politically polarizing issues are a growing concern around the world, creating divisions along ideological lines, which was also confirmed during the 2022 United States midterm elections. The purpose of this study was to explore the relationship between the results of the 2022 U.S. midterm elections and the topics that were covered during the campaign. A dataset consisting of 52,688 tweets in total was created by collecting tweets of senators, representatives and governors who participated in the elections one month before the start of the elections. Using unsupervised machine learning, topic modeling is built on the collected data and visualized to represent topics. Furthermore, supervised machine learning is used to classify tweets to the corresponding political party, whereas sentiment analysis is carried out in order to detect polarity and subjectivity. Tweets from participating politicians, U.S. states and involved parties were found to correlate with polarizing topics. This study hereby explored the relationship between the topics that were creating a divide between Democrats and Republicans during their campaign and the 2022 U.S. midterm election outcomes. This research found that polarizing topics permeated the Twitter (today known as X) campaign, and that all elections were classified as highly subjective. In the Senate and House elections, this classification analysis showed significant misclassification rates of 21.37% and 24.15%, respectively, indicating that Republican tweets often aligned with traditional Democratic narratives.
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页数:28
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共 74 条
  • [1] Abramowitz A.I., 2012, The US Senate: From Deliberation to Dysfunction, V1st ed., P31, DOI [10.4135/9781483349459, DOI 10.4135/9781483349459]
  • [2] United States: Racial Resentment, Negative Partisanship, and Polarization in Trump's America
    Abramowitz, Alan
    McCoy, Jennifer
    [J]. ANNALS OF THE AMERICAN ACADEMY OF POLITICAL AND SOCIAL SCIENCE, 2019, 681 (01) : 137 - 156
  • [3] Al-Shabi MA, 2020, INT J COMPUT SCI NET, V20, P51
  • [4] A large-scale sentiment analysis of tweets pertaining to the 2020 US presidential election
    Ali, Rao Hamza
    Pinto, Gabriela
    Lawrie, Evelyn
    Linstead, Erik J.
    [J]. JOURNAL OF BIG DATA, 2022, 9 (01)
  • [5] Allison N., 2022, Politico
  • [6] Design and Implementation of a Machine Learning-Based Authorship Identification Model
    Anwar, Waheed
    Bajwa, Imran Sarwar
    Ramzan, Shabana
    [J]. SCIENTIFIC PROGRAMMING, 2019, 2019
  • [7] Argus C., 2022, Gabe Vasquez Wins Race for New Mexico's 2nd Congressional District, Carlsbad Current Argus
  • [8] Ausubel J.R., 2019, CUREJ-College Undergraduate Research Electronic Journal, P89
  • [9] Why Has US Policy Uncertainty Risen Since 1960?
    Baker, Scott R.
    Bloom, Nicholas
    Canes-Wrone, Brandice
    Davis, Steven J.
    Rodden, Jonathan
    [J]. AMERICAN ECONOMIC REVIEW, 2014, 104 (05) : 56 - 60
  • [10] Bamman D., 2015, P 29 INT ASS ADV ART, VVolume 9, P574, DOI [DOI 10.1609/ICWSM.V9I1.14655, 10.1609/icwsm.v9i1.14655]