ReelFramer: Human-AI Co-Creation for News-to-Video Translation
This addresses the challenge for news outlets to reach young audiences on social media by adapting journalistic content into short videos, though it is incremental in applying narrative framing to a specific domain.
The paper tackled the problem of translating traditional news articles into engaging short videos for social media by developing ReelFramer, a human-AI co-creative system that helps journalists explore narrative framings and generate scripts and storyboards, resulting in more relevant and coherent scripts.
Short videos on social media are the dominant way young people consume content. News outlets aim to reach audiences through news reels -- short videos conveying news -- but struggle to translate traditional journalistic formats into short, entertaining videos. To translate news into social media reels, we support journalists in reframing the narrative. In literature, narrative framing is a high-level structure that shapes the overall presentation of a story. We identified three narrative framings for reels that adapt social media norms but preserve news value, each with a different balance of information and entertainment. We introduce ReelFramer, a human-AI co-creative system that helps journalists translate print articles into scripts and storyboards. ReelFramer supports exploring multiple narrative framings to find one appropriate to the story. AI suggests foundational narrative details, including characters, plot, setting, and key information. ReelFramer also supports visual framing; AI suggests character and visual detail designs before generating a full storyboard. Our studies show that narrative framing introduces the necessary diversity to translate various articles into reels, and establishing foundational details helps generate scripts that are more relevant and coherent. We also discuss the benefits of using narrative framing and foundational details in content retargeting.