Social Media User Engagement for Promoting Public Health: Pattern Analysis Using Apriori Approach

User engagement has become an essential aspect of research in technology, marketing, and social media. Measuring and analyzing user engagement has gained significant importance due to its ability to provide valuable insights to assess engagement responses from online users engaged in published content. Previous studies have devised various methods to effectively analyze data, with statistical approaches commonly used as techniques. However, statistical approaches often struggle to effectively handle categorical variables, for which interpretation becomes limited and less informative, also failing to capture the structure and dependencies inherent in categorical data. Therefore, Apriori approach that defines association rules to generate patterns based on user frequent itemset is adapted to identify UGC patterns for user engagement in public health social media. This study aims to generate association rules based on UGC data and identify UGC patterns for user engagement. The frequency of UGC data such as like, link click, share, and comment was analyzed using Apriori approach with support and confidence thresholds. The result shows that 16 association rules were generated based on UGC data. Five with 100%, three with 90%, one with 60%, three with 50%, one with 40%, and three with 30%. It can be concluded that the Apriori approach can be used in identifying the pattern of user engagement on public health social media other than buying and selling of market basket transactions.