Transmedia storytelling, defined as the narrative practice that unfolds across multiple platforms, has transformed audience engagement in the era of media convergence. This study aims to examine the dynamics of audience participation, emphasizing patterns, emotional responses, and engagement trends through advanced quantitative techniques. Leveraging Social Network Analysis (Kalogeras, 2014), sentiment analysis, and predictive modeling, this research analyzes audience behavior using real-time data from social media platforms such as Twitter, Weibo, and Reddit, focusing on transmedia campaigns like Stranger Things and The King’s Avatar. This methodology collected and processed 20,000 social media posts and 1,305 survey responses. SNA found that 18 percent of users accounted for 65 percent of content dissemination, and four distinct clusters of audience. An overwhelmingly positive sentiment was found in the sentiment analysis, 82% with positive sentiment towards interactive elements like the Surfer Boy Pizza hotline, 10% neutral and 8% negative sentiment mainly focusing on narrative fragmentation. The strongest predictor of engagement was sentiment score (β=0.79) followed by multimedia content (β=0.52) in predictive modeling. Text only posts fared poorly compared to posts that featured multimedia, posting a 45% lower engagement rate. The findings highlight the importance of the influence of key influencers, emotionally resonant content and platform specific strategies in order to maximize the audience participation. The research contributes actionable strategies to further transmedia storytelling campaigns by addressing challenges such as fragmented participation and narrative coherence. The lessons from these insights apply across industries, including entertainment, education and advocacy, providing a data driven approach to navigate the intricacies of audience dynamics in a convergent media environment.