KEYWORDS: Data modeling, Machine learning, Data communications, Image classification, Performance modeling, Web 2.0 technologies, Analytical research, Random forests, Education and training, Image fusion
This study examines the factors influencing and predicts the cultural communication on social media, using the Palace Museum's Weibo account as a case study. Machine learning techniques are employed to analyze data including microblog content and user interactions. Factors considered include content diversity, sentiment polarity, topic popularity, and user engagement behaviors. Predictive models achieve 85% accuracy in determining the influence of content diversity, sentiment polarity, and topic popularity. User engagement behaviors, such as reposts and comments, positively correlate with dissemination effectiveness. Findings emphasize the importance of diverse content and positive sentiment in enhancing dissemination, as well as the significance of user interactions. Practical implications for cultural institutions in leveraging social media platforms are highlighted, emphasizing optimization of content diversity, sentiment expression, and encouraging user engagement.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.