This thesis investigates the dynamics of crowdfunding success in Kickstarter’s technology category using a blend of network analysis and machine learning. By sourcing a comprehensive dataset from Webrobots.io, which includes detailed monthly data dumps of Kickstarter projects, this study focuses on United States (U.S.)-based technology projects to uncover patterns and factors contributing to successful crowdfunding outcomes. The methodology encompasses rigorous data preprocessing, advanced feature engineering, and graph-based modeling to represent relationships between projects, creators, and subcategories. Network analysis techniques, including centrality measures and community detection, identify influential projects and creators and uncover clusters with similar characteristics. A RandomForest machine learning model, integrating project-specific metrics and network-derived features, predicts project success with high accuracy. Findings reveal significant patterns in project features, creator influence, and community structures that impact crowdfunding success. The predictive model serves as a practical tool for guiding project development and marketing strategies. This thesis enhances the understanding of crowdfunding platforms through the integration of network science and predictive analytics, offering insights for creators, backers, and platform moderators. The enriched dataset and methodologies are made publicly accessible to encourage further research and practical applications in crowdfunding.
Unveiling the Dynamics of Crowdfunding Success: A Network Analysis and Machine Learning Approach to Kickstarter Technology Projects
