@article {167940,
	title = {Uncovering Topics of Public Cultural Activities: Evidence from China},
	journal = {Data Intelligence},
	volume = {4},
	year = {2022},
	month = {2022/07/01/},
	pages = {509 - 528},
	abstract = {In this study, we uncover the topics of Chinese public cultural activities in 2020 with a two-step short text clustering (self-taught neural networks and graph-based clustering) and topic modeling approach. The dataset we use for this research is collected from 108 websites of libraries and cultural centers, containing over 17,000 articles. With the novel framework we propose, we derive 3 clusters and 8 topics from 21 provinciallevel regions in China. By plotting the topic distribution of each cluster, we are able to shows unique tendencies of local cultural institutes, that is, free lessons and lectures on art and culture, entertainment and service for socially vulnerable groups, and the preservation of intangible cultural heritage respectively. The findings of our study provide decision-making support for cultural institutes, thus promoting public cultural service from a data-driven perspective.},
	keywords = {Big data, Big data analyse, big data analysis, Cluster analysis, Decision making, Graphic methods, Historic preservation, LDA, Network-based, Networks and graphs, Neural-networks, Public culture, Short text clustering, Short texts, Text Clustering, Topic Modeling},
	isbn = {20967004 (ISSN)},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136325868\&doi=10.1162\%2fdint_a_00121\&partnerID=40\&md5=a669989984b21f51c2cbd285bc5667a1},
	author = {Zeng, Zixin and Hua, Bolin}
}
