I know your stance! Analyzing Twitter users’ political stance on diverse perspectives
Abstract The popularity of social network service users has increased in recent years, altering politicians’ interest level in social network services. Given this trend, social network services now play a central role in political communication channels, enabling them to express and share their opin...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-01-01
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| Series: | Journal of Big Data |
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| Online Access: | https://doi.org/10.1186/s40537-025-01083-z |
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| _version_ | 1832585647308668928 |
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| author | Jisu Kim Dongjae Kim Eunil Park |
| author_facet | Jisu Kim Dongjae Kim Eunil Park |
| author_sort | Jisu Kim |
| collection | DOAJ |
| description | Abstract The popularity of social network service users has increased in recent years, altering politicians’ interest level in social network services. Given this trend, social network services now play a central role in political communication channels, enabling them to express and share their opinions and news directly with citizens. Therefore, many researchers have attempted to investigate social network service users’ political stances and proposed a user’s political stance model utilizing this dataset. Understanding and detecting social network services and a user’s political stance can play a significant role in marketing strategies and determining election winners. In light of this, the present study examined Twitter from diverse perspectives to analyze and detect a Twitter user’s political stance. This study collected Twitter datasets and labeled a user’s stance using a clustering approach to determine whether a user was a Democrat or a Republican. After an exploratory analysis of users’ tweet content: image and text, user network, and profile description, the tweet user stance detection model was proposed and tested. The results indicated notable differences between Democrats and Republicans from diverse perspectives on Twitter, with an accuracy of 85.35% compared with baseline models. The implications and limitations of this study were discussed based on the results. |
| format | Article |
| id | doaj-art-8492fcfd22f6468b8944e972967f585b |
| institution | Kabale University |
| issn | 2196-1115 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj-art-8492fcfd22f6468b8944e972967f585b2025-01-26T12:37:43ZengSpringerOpenJournal of Big Data2196-11152025-01-0112113010.1186/s40537-025-01083-zI know your stance! Analyzing Twitter users’ political stance on diverse perspectivesJisu Kim0Dongjae Kim1Eunil Park2Department of Interaction Science, Sungkyunkwan UniversityDepartment of Applied Artificial Intelligence, Sungkyunkwan UniversityDepartment of Interaction Science, Sungkyunkwan UniversityAbstract The popularity of social network service users has increased in recent years, altering politicians’ interest level in social network services. Given this trend, social network services now play a central role in political communication channels, enabling them to express and share their opinions and news directly with citizens. Therefore, many researchers have attempted to investigate social network service users’ political stances and proposed a user’s political stance model utilizing this dataset. Understanding and detecting social network services and a user’s political stance can play a significant role in marketing strategies and determining election winners. In light of this, the present study examined Twitter from diverse perspectives to analyze and detect a Twitter user’s political stance. This study collected Twitter datasets and labeled a user’s stance using a clustering approach to determine whether a user was a Democrat or a Republican. After an exploratory analysis of users’ tweet content: image and text, user network, and profile description, the tweet user stance detection model was proposed and tested. The results indicated notable differences between Democrats and Republicans from diverse perspectives on Twitter, with an accuracy of 85.35% compared with baseline models. The implications and limitations of this study were discussed based on the results.https://doi.org/10.1186/s40537-025-01083-zPolitical stanceTweet user stance modelTwitterMachine learning |
| spellingShingle | Jisu Kim Dongjae Kim Eunil Park I know your stance! Analyzing Twitter users’ political stance on diverse perspectives Journal of Big Data Political stance Tweet user stance model Machine learning |
| title | I know your stance! Analyzing Twitter users’ political stance on diverse perspectives |
| title_full | I know your stance! Analyzing Twitter users’ political stance on diverse perspectives |
| title_fullStr | I know your stance! Analyzing Twitter users’ political stance on diverse perspectives |
| title_full_unstemmed | I know your stance! Analyzing Twitter users’ political stance on diverse perspectives |
| title_short | I know your stance! Analyzing Twitter users’ political stance on diverse perspectives |
| title_sort | i know your stance analyzing twitter users political stance on diverse perspectives |
| topic | Political stance Tweet user stance model Machine learning |
| url | https://doi.org/10.1186/s40537-025-01083-z |
| work_keys_str_mv | AT jisukim iknowyourstanceanalyzingtwitteruserspoliticalstanceondiverseperspectives AT dongjaekim iknowyourstanceanalyzingtwitteruserspoliticalstanceondiverseperspectives AT eunilpark iknowyourstanceanalyzingtwitteruserspoliticalstanceondiverseperspectives |