Visualizing Political Communication Trends across Generations on X (Twitter): Insights Through Topic Modeling and Word Clouds

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dc.contributor.authorUdomwisanpat, Prinwat
dc.contributor.authorJitkajornwanich, Kulsawasd
dc.contributor.authorKraishan, Obada
dc.contributor.authorSrestasatheirn, Panu
dc.contributor.authorLawawirojwong, Siam
dc.contributor.authorCharoenporn, Pattama
dc.date.accessioned2026-08-06T10:45:19Z
dc.date.available2026-08-06T10:45:19Z
dc.date.issued2024-01-01
dc.description.abstractThis study examines the interests and significance of words on Twitter (or X) across different generational groups: Baby Boomers, Generation X, Generation Y, and Generation Z. Using Topic Modeling with Latent Dirichlet Allocation (LDA), the research explores relationships and word importance within each group. As the results of topic modeling are not always easy to interpret, we used word cloud visualization to help make sense of the results for each generation. The findings reveal distinct patterns: Baby Boomers frequently mention print media, news websites, and prominent Thai political figures; Generation X emphasizes individuals and local political issues in Bangkok; Generation Y discusses political and social events; and Generation Z uniquely questions political and social activities. This research methodology is applicable across languages and tasks, offering insights into generational behaviors and interests.
dc.identifier.citationProceedings 2024 IEEE Wic International Conference on Web Intelligence and Intelligent Agent Technology Wi Iat 2024, 665-670, 2024
dc.identifier.doi10.1109/WI-IAT62293.2024.00107
dc.identifier.other2-s2.0-105007148675
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/15433
dc.sourceProceedings 2024 IEEE Wic International Conference on Web Intelligence and Intelligent Agent Technology Wi Iat 2024
dc.subjectLatent Dirichlet Allocation
dc.subjectNatural Language Processing
dc.subjectPolitical Communication
dc.subjectTag Cloud
dc.subjectTopic Modeling
dc.subjectWord Cloud
dc.titleVisualizing Political Communication Trends across Generations on X (Twitter): Insights Through Topic Modeling and Word Clouds
dc.typeConference Paper

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