The field of online moderation and deliberation is moving towards more scalable and effective methods for evaluating and improving online discussions. Researchers are exploring the use of synthetic simulations and large language models to bypass the need for human participation in experiments, and to develop more efficient and effective moderation strategies. There is also a growing interest in representing diverse viewpoints and promoting high-quality discussions in online comment sections. Additionally, the development of conversational coaching agents is focusing on core functionality and user-centered design. Notable papers in this area include:

• A study on scalable evaluation of online moderation strategies via synthetic simulations, which proposed a novel methodology using large language models and introduced an open-source framework and dataset.
• A paper on representative ranking for deliberation in the public sphere, which introduced guarantees of representation into algorithmic ranking methods to promote diverse viewpoints and high-quality discussions.