Communicating science and technology is essential for the public to understand and engage in a rapidly changing world. Tweetorials are an emerging phenomenon where experts explain STEM topics on social media in creative and engaging ways. However, STEM experts struggle to write an engaging "hook" in the first tweet that captures the reader's attention. We propose methods to use large language models (LLMs) to help users scaffold their process of writing a relatable hook for complex scientific topics. We demonstrate that LLMs can help writers find everyday experiences that are relatable and interesting to the public, avoid jargon, and spark curiosity. Our evaluation shows that the system reduces cognitive load and helps people write better hooks. Lastly, we discuss the importance of interactivity with LLMs to preserve the correctness, effectiveness, and authenticity of the writing.

Short videos on social media are the dominant way young people consume content. News outlets would like to reach audiences through news reels - short videos that convey news - but struggle to translate traditional journalistic formats into short, colloquial videos. Generative AI has the potential to transform content but often fails to be correct and coherent by itself. To help journalists create scripts and storyboards for news reels, we introduce a human-AI co-creative system called ReelFramer. It uses an intermediate step of framing and foundation to guide AI toward better outputs. We introduce three narrative framings to balance information and entertainment in news reels. The foundation for the script is a premise, and the foundation for the storyboard is a character board. Our studies show that the premise helps generate more relevant and coherent scripts and that co-creating with AI lowers journalists' barriers to making their first news reels.