The popularity and importance of microblogging services like Twitter has drawn the attention of both scholars and practitioners. While previous research has explored the adoption of Twitter in business markets (Cripps et al., 2020; Juntunen et al., 2020) and its impact in various contexts such as disaster management and emergence awareness (Martínez-Rojas et al., 2018; Xu, 2020), there remains a knowledge gap in understanding the antecedents of topic popularity based on both social ties and emotional sentiments. To fill the gap, this study aims to empirically investigate how social ties and sentiment articulated in social media contents on microblogs promote topic popularity. Specifically, this study examines the impacts of user social ties and sentiment of social media contents (e.g. positive or negative sentiment) on information sharing and diffusion through Twitter, resulting in popularity of trending topics. We initially collected 0.73 million georeferenced tweets data from Twitter using a location-based social media (LBSM). We then further retrieved the sentiment data and created social ties based on the hashtag of each tweet and geographic location. An econometric panel data analysis will be conducted to predict the tweeting behavior. Tweets-specific fixed effect will be used to run the data analysis. This research contributes to the information systems (IS) field by revealing insights into the dynamics of information sharing dynamics. In addition, the findings may provide practical implications for businesses, offering insights that can help to tailor their content strategies for maximizing reach and engagement on social media platforms.