AI-Enhanced Big Data Content Analysis of Weibo for Digital Cultural Heritage: Insights into Sichuan Opera
DOI:
https://doi.org/10.63313/SSH.9080Keywords:
Weibo analytics, Digital cultural heritage, Big data analysis, Sichuan Opera preservationAbstract
Sichuan Opera, a prominent Chinese intangible cultural heritage, is celebrated for its diverse genres, distinctive vocal and acrobatic performance styles, and rich musical traditions. However, in the digital era it faces challenges such as audience decline and difficulties in cultural transmission due to competition from new media and globalization. Preserving Sichuan Opera is crucial for maintaining cultural diversity and fostering social innovation. In this study, we leverage digital technologies to analyze and support its preservation. Weibo, a leading Chinese social media platform, was used as a data source to capture public discourse related to Sichuan Opera. After collecting and preprocessing thousands of relevant Weibo posts, we applied natural language processing (NLP) and machine learning techniques to encode, cluster, and analyze the text data. Through combined qualitative and quantitative analysis, we identify key thematic topics, geographic distribution patterns, and publication trends in Sichuan Opera’s online presence. Our AI-driven big data analysis reveals current strengths and gaps in the Opera’s social media dissemination. The results provide actionable insights for enhancing audience engagement and informing digital strategies to sustain Sichuan Opera, demonstrating the potential of social media analytics and AI in the knowledge management and conservation of intangible cultural heritage. The methodology could also guide digital preservation efforts for other traditional operatic art forms and cultural media.
References
[1] McDermott, P. (2010). Building open government. Government Information Quarterly, 27(4), 401-413.
[2] Mergel, I. (2012). The social media innovation challenge in the public sector. Information polity, 17(3-4), 281-292.
[3] Nian, F., Li, J., Diao, H., & Yu, X. (2022). Weibo core user mining and propagation scale predicting. Chaos, Solitons & Fractals, 156, 111869.
[4] Zaefarian, G., Misra, S., Koval, M., & Iurkov, V. (2022). Social network analysis in marketing: A step-by-step guide for researchers. Industrial Marketing Management, 107, A11-A24.
[5] Verdú, M. J., De Castro, J. P., Regueras, L. M., & Corell, A. (2021). MSocial: practical integration of social learning analytics into Moodle. IEEE Access, 9, 23705-23716.
[6] Singh, S. S., Muhuri, S., Mishra, S., Srivastava, D., Shakya, H. K., & Kumar, N. (2024). Social Network Analysis: A Survey on Process, Tools, and Application. ACM Computing Surveys, 56(8), 1-39.
[7] Alwash, N., & Levine, J. D. (2019). Network analyses reveal structure in insect social groups. Current opinion in insect science, 35, 54-59.
[8] Zhang, H., Fiszman, M., Shin, D., Miller, C. M., Rosemblat, G., & Rindflesch, T. C. (2011). Degree centrality for semantic abstraction summarization of therapeutic studies. Journal of biomedical informatics, 44(5), 830-838.
[9] Zhang, J., & Luo, Y. (2017, March). Degree centrality, betweenness centrality, and closeness centrality in social network. In 2017 2nd international conference on modelling, simulation and applied mathematics (MSAM2017) (pp. 300-303). Atlantis press.
[10] Zhao, F., Ren, X., Yang, S., Han, Q., Zhao, P., & Yang, X. (2020). Latent dirichlet allocation model training with differential privacy. IEEE Transactions on Information Forensics and Security, 16, 1290-1305.
[11] Jelodar, H., Wang, Y., Yuan, C., Feng, X., Jiang, X., Li, Y., & Zhao, L. (2019). Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey. Multimedia tools and applications, 78, 15169-15211.
[12] Tong, Z., & Zhang, H. (2016, May). A text mining research based on LDA topic modelling. In International conference on computer science, engineering and information technology (pp. 201-210).
[13] Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of machine Learning research, 3(Jan), 993-1022.
[14] Xie, R., Chu, S. K. W., Chiu, D. K. W., & Wang, Y. (2021). Exploring public response to COVID-19 on Weibo with LDA topic modeling and sentiment analysis. Data and Information Management, 5(1), 86-99.
[15] Liu, X., & Hu, W. (2019). Attention and sentiment of Chinese public toward green buildings based on Sina Weibo. Sustainable cities and society, 44, 550-558.
[16] García, S., Ramírez-Gallego, S., Luengo, J., Benítez, J. M., & Herrera, F. (2016). Big data preprocessing: methods and prospects. Big data analytics, 1, 1-22.
[17] Gharatkar, S., Ingle, A., Naik, T., & Save, A. (2017, March). Review preprocessing using data cleaning and stemming technique. In 2017 international conference on innovations in information, embedded and communication systems (iciiecs) (pp. 1-4). IEEE.
[18] Ahmed, I., & Ishtiaq, S. (2021). Reliability and Validity: Importance in medical research. Methods, 12(1), 2401-2406.
[19] Śmietańska, K., & Podziewski, P. (2019). The use of the Krippendorff’s coefficient in determining intra-rater reliability in human visual quality control of furniture manufacturing processes. Annals of Warsaw University of Life Sciences SGGW Forestry and Wood Technology, 108, 97-103.
[20] Chen, J., Song, N., Su, Y., Zhao, S., & Zhang, Y. (2022). Learning user sentiment orientation in social networks for sentiment analysis. Information Sciences, 616, 526-538.
[21] Ray, P., & Chakrabarti, A. (2017, February). Twitter sentiment analysis for product review using lexicon method. In 2017 International Conference on Data Management, Analytics and Innovation (ICDMAI) (pp. 211-216). IEEE.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 by author(s) and Erytis Publishing Limited.
This work is licensed under a Creative Commons Attribution 4.0 International License.
