Polarizing Topics on Twitter in the 2022 United States Elections

IF 2.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Information (Switzerland) Pub Date : 2023-11-10 DOI:10.3390/info14110609
Josip Katalinić, Ivan Dunđer, Sanja Seljan
{"title":"Polarizing Topics on Twitter in the 2022 United States Elections","authors":"Josip Katalinić, Ivan Dunđer, Sanja Seljan","doi":"10.3390/info14110609","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":38479,"journal":{"name":"Information (Switzerland)","volume":" 3","pages":"0"},"PeriodicalIF":2.4000,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information (Switzerland)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/info14110609","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0

Abstract

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.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
2022年美国大选中推特上两极分化的话题
政治两极化问题在世界范围内日益受到关注,在意识形态上产生分歧,这在2022年美国中期选举中也得到了证实。本研究的目的是探讨2022年美国中期选举结果与竞选期间所涉及的主题之间的关系。在选举开始前一个月,通过收集参加选举的参议员、众议员、州长的推文,建立了52688条推文的数据集。使用无监督机器学习,主题建模建立在收集的数据上,并可视化地表示主题。此外,使用监督机器学习将推文分类到相应的政党,而进行情感分析以检测极性和主观性。来自参与的政治家、美国各州和相关政党的推文被发现与两极分化的话题相关。因此,本研究探讨了在竞选期间造成民主党和共和党之间分歧的话题与2022年美国中期选举结果之间的关系。这项研究发现,两极分化的话题充斥着Twitter(今天被称为X)的竞选活动,所有的选举都被归类为高度主观的。在参议院和众议院选举中,这种分类分析显示,错误分类率分别为21.37%和24.15%,这表明共和党的推文往往与民主党的传统叙事保持一致。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Information (Switzerland)
Information (Switzerland) Computer Science-Information Systems
CiteScore
6.90
自引率
0.00%
发文量
515
审稿时长
11 weeks
期刊最新文献
Weakly Supervised Learning Approach for Implicit Aspect Extraction Science Mapping of Meta-Analysis in Agricultural Science An Integrated Time Series Prediction Model Based on Empirical Mode Decomposition and Two Attention Mechanisms Context-Aware Personalization: A Systems Engineering Framework Polarizing Topics on Twitter in the 2022 United States Elections
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1