Visualizing Political Communication Trends across Generations on X (Twitter): Insights Through Topic Modeling and Word Clouds - Udomwisanpat, Prinwat; Jitkajornwanich, Kulsawasd; Kraishan, Obada; Srestasatheirn, Panu; Lawawirojwong, Siam; Charoenporn, Pattama (My University, 2024)
This 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.
Keywords: Latent Dirichlet Allocation; Natural Language Processing; Political Communication; Tag Cloud; Topic Modeling; Word Cloud