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.

翻译：科学技术的传播对于公众理解和参与快速变化的世界至关重要。推文教程是一种新兴现象，专家通过社交媒体以创造性且引人入胜的方式解释STEM主题。然而，STEM专家在撰写第一条推文中能够吸引读者注意力的“钩子”时面临困难。我们提出利用大型语言模型（LLM）的方法，帮助用户构建为复杂科学主题撰写通俗易懂钩子的过程。我们证明，LLM能够帮助写作者找到公众可共鸣且有趣的日常体验，避免使用专业术语，并激发好奇心。我们的评估表明，该系统能降低认知负荷，并帮助用户撰写更好的钩子。最后，我们讨论了与LLM进行交互的重要性，以保持写作的正确性、有效性和真实性。