User sentiment dynamics in social media: a comparative analysis of X and Threads

This research examines the dynamics of user sentiment and its correlation with the usage factors of applications in the context of the competition between X (formerly Twitter) and Threads, a social media application under the umbrella of Meta. Through sentiment analysis of user reviews on the Google Play Store and App Store, the study aims to identify the key factors contributing to a significant decline in user engagement with Threads and the return of users to X. The method employed in this research is the support vector machine (SVM) for sentiment classification of reviews. The study then correlates the classified sentiments with application usage factors: usability, features, design, and support. The research findings indicate user sentiment influences user engagement, especially in features and design. The research concludes with insights regarding implications for application developers and suggests directions for future research.

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Publication Details

Journal
IAES International Journal of Artificial Intelligence
Published
2024-11-29
DOI
https://doi.org/10.11591/ijai.v14.i1.pp447-456
Citations
1
Primary Topic
Digital Marketing and Social Media
Type
article
Field-Weighted Citation Impact
1.13

Funders

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article

User sentiment dynamics in social media: a comparative analysis of X and Threads

Yova Ruldeviyani, Rezki Khairunnas, Jeri Apriansyah Pagua, Ghina Fitriya
1 citations
IAES International Journal of Artificial Intelligence
Digital Marketing and Social Media
1.13
article

User sentiment dynamics in social media: a comparative analysis of X and Threads

Yova Ruldeviyani, Rezki Khairunnas, Jeri Apriansyah Pagua, Ghina Fitriya
article en
1 citations

Abstract

This research examines the dynamics of user sentiment and its correlation with the usage factors of applications in the context of the competition between X (formerly Twitter) and Threads, a social media application under the umbrella of Meta. Through sentiment analysis of user reviews on the Google Play Store and App Store, the study aims to identify the key factors contributing to a significant decline in user engagement with Threads and the return of users to X. The method employed in this research is the support vector machine (SVM) for sentiment classification of reviews. The study then correlates the classified sentiments with application usage factors: usability, features, design, and support. The research findings indicate user sentiment influences user engagement, especially in features and design. The research concludes with insights regarding implications for application developers and suggests directions for future research.

IAES International Journal of Artificial IntelligenceVol. 14(1)
University of Indonesia (ID)
Ministry of Communication and Information Technology
Openalex Percentile: Top 15%
Digital Marketing and Social Media
1.13
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