Integrating Big Data and Thick Data Analysis for User Experience Evaluation in Cultural Curation: a Case Study of the 2023 Hakka Expo in Taiwan

EasyChair Preprint 15669

Abstract

   This study presents an integrated approach to evaluating user experience (UX) in cultural curation by combining big data and thick data analyses, with a focus on the 2023 Hakka Expo in Taiwan (2023 HEIT). Big data plays a pivotal role in identifying UX hotspots and directing future research, while thick data, derived from sources such as user interviews, curatorial proposals, news reports, and social media exchanges, captures the depth of the UX. The fusion of these two data types provides a holistic view of UX, with big data offering quantitative metrics and thick data delivering qualitative insights into emotions and subtleties.

   Enhanced by a Large Language Model (LLM), the findings reveal that users valued the pragmatic and hedonic design elements of the 2023 HEIT. The LLM's analysis of user feedback and comments provided a detailed understanding of UX, identifying areas of satisfaction and potential enhancements, which aligns with research highlighting LLMs' effectiveness in uncovering user preferences and emotional responses. However, a comparison of high-frequency keywords from thick data sources with those from interview scripts revealed a discrepancy, indicating that the Expo's main messages did not always resonate with visitors. Future research will aim to incorporate more data and a broader range of sources to better understand the combination of big and thick data in studying cultural event experiences.

Keyphrases: Big Data, Cultural Curation, Thick Data, large language models, user experience

@booklet{EasyChair:15669,
  author    = {Sheng-Ming Wang and Yuan-Chen Zhang},
  title     = {Integrating Big Data and Thick Data Analysis for User Experience Evaluation in Cultural Curation: a Case Study of the 2023 Hakka Expo in Taiwan},
  howpublished = {EasyChair Preprint 15669},
  year      = {EasyChair, 2025}}