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Detecting Public Issues in the 2024 East Java Gubernatorial Election through LDA Topic Modeling on Social Media X

Author

Listed:
  • Aries Dwi Indriyanti
  • Rahmat Gernowo
  • Eko Sediyono
  • Mahrus Ali

Abstract

General elections are one of the pillars of democracy, so understanding public issues developing in the digital space is crucial to strengthening the legitimacy and quality of their implementation. The 2024 East Java gubernatorial election presents a strategic opportunity given the high level of regional political dynamics and the use of social media as a platform for political discourse. This study aims to identify key issues in public conversations on social media platforms related to the election, map their thematic distribution, and illustrate the distribution of public opinion across each topic. The research method used topic modeling analysis based on Latent Dirichlet Allocation. Data was collected from 3,500 public posts relevant to the 2024 East Java gubernatorial election. The analysis process included text data cleaning, tokenization, removal of common words, and model development to obtain optimal topics. The results showed a model coherence value of 0.51, with three main topics: First, the implementation of regional head elections, including technical aspects, regional context, and the role of election organizers. Second, the security and smoothness of the election process, including voter management and trust in the results. Third, voter participation and the role of election organizers in ensuring legitimacy. The distribution of topics varies by region, influenced by local political backgrounds and previous election experiences. In conclusion, social media is a crucial arena for shaping public opinion and disseminating political issues. Digital data-based thematic analysis can help election organizers and policymakers design more effective public communications, increase participation, and strengthen public trust in elections at the regional level.

Suggested Citation

Handle: RePEc:dbk:datame:v:4:y:2025:i::p:1140:id:1056294dm20251140
DOI: 10.56294/dm20251140
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