ReelFramer: Co-creating News Reels on Social Media with Generative AI

Short videos on social media are a prime way many young people find and consume content. News outlets would like to reach audiences through news reels, but currently struggle to translate traditional journalistic formats into the short, entertaining videos that match the style of the platform. There are many ways to frame a reel-style narrative around a news story, and selecting one is a challenge. Different news stories call for different framings, and require a different trade-off between entertainment and information. We present a system called ReelFramer that uses text and image generation to help journalists explore multiple narrative framings for a story, then generate scripts, character boards and storyboards they can edit and iterate on. A user study of five graduate students in journalism-related fields found the system greatly eased the burden of transforming a written story into a reel, and that exploring framings to find the right one was a rewarding process.