The Storytelling project uses machine-based analytics to identify the qualities of engaging and marketable media. By developing models with the ability to “read” emotional arcs and semantic narrative video content, our researchers aim to map video story structure across many story types and formats.
To complement this content-based analysis, our researchers are also developing methods to analyze how emotional and semantic narratives affect viewer engagement with these stories. By tracking “referrals” of video URLs on social media networks, our researchers hope to identify how stories of different types and genres diffuse across networks, who influences this spread, and how video story distribution might be optimized. Given this project’s two-pronged strategy, our hope is to develop a robust story learning machine that uniquely maps the relationship between story structure and engagement across networks.