# Tweetorial Hooks: Generative AI Tools to Motivate Science on Social Media

arXiv: https://arxiv.org/abs/2305.12265 · HTML: https://mlsanity.com/paper/2305.12265/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 37 references that are in the map.
- First posted on arXiv 20 May 2023.

## Builds on (most cited first, 9 of 9)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts](https://mlsanity.com/paper/2110.01691/) (2021)
- [Sparks: Inspiration for Science Writing using Language Models](https://mlsanity.com/paper/2110.07640/) (2021)
- [Comparison of Diverse Decoding Methods from Conditional Language Models](https://mlsanity.com/paper/1906.06362/) (2019)
- [Hooks in the Headline: Learning to Generate Headlines with Controlled Styles](https://mlsanity.com/paper/2004.01980/) (2020)
- [PopBlends: Strategies for Conceptual Blending with Large Language Models](https://mlsanity.com/paper/2111.04920/) (2021)
- [Clickbait? Sensational Headline Generation with Auto-tuned Reinforcement Learning](https://mlsanity.com/paper/1909.03582/) (2019)
- [Advances of Transformer-Based Models for News Headline Generation](https://mlsanity.com/paper/2007.05044/) (2020)

## Built on by (most cited first, 11 of 11)

- [LLMs as Research Tools: A Large Scale Survey of Researchers' Usage and Perceptions](https://mlsanity.com/paper/2411.05025/) (2024)
- [ABScribe: Rapid Exploration & Organization of Multiple Writing Variations in Human-AI Co-Writing Tasks using Large Language Models](https://mlsanity.com/paper/2310.00117/) (2023)
- [Designing LLM Chains by Adapting Techniques from Crowdsourcing Workflows](https://mlsanity.com/paper/2312.11681/) (2023)
- [Not Just Novelty: A Longitudinal Study on Utility and Customization of an AI Workflow](https://mlsanity.com/paper/2402.09894/) (2024)
- [Which Contributions Deserve Credit? Perceptions of Attribution in Human-AI Co-Creation](https://mlsanity.com/paper/2502.18357/) (2025)
- [A Systematic Review of Human-AI Co-Creativity](https://mlsanity.com/paper/2506.21333/) (2025)
- [Evaluating Style-Personalized Text Generation: Challenges and Directions](https://mlsanity.com/paper/2508.06374/) (2025)
- [FeedQUAC: Quick Unobtrusive AI-Generated Commentary](https://mlsanity.com/paper/2504.16416/) (2025)
- [PortfolioMentor: Multimodal Generative AI Companion for Learning and Crafting Interactive Digital Art Portfolios](https://mlsanity.com/paper/2311.14091/) (2023)
- [RevTogether: Supporting Science Story Revision with Multiple AI Agents](https://mlsanity.com/paper/2503.01608/) (2025)
- [Facilitating Longitudinal Interaction Studies of AI Systems](https://mlsanity.com/paper/2508.10252/) (2025)

## Abstract (arXiv)

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.