All of our activities centered around developing a very first version of a digital twin of online social networks. We did this in a modular way, such that components can be developed in parallel, but also changed and/or replaced by future, improved versions without compromising the overall infrastructure. In broad strokes, next to supporting work like collecting necessary datasets, performing evaluations, and so on, we did the following:
(1) We started development a "platform model". This involves creating a network generator that can generate different types of networks with different characteristics, which then can be used to simulate, for instance, different news feeds and different ways of users connecting to each other. In particular, we showed the importance of extending existing network models with "success-driven activity", illustrating that outcomes such as polarization change if previous success of agents is taken into account in a simulation.
(2) We started developing a "user model". In particular, we developed agents that, based on large language models, can react to content on social networks, according to different persona's (e.g. based on their political leaning or other features). These agents of bots can be linked up both to simulation models as well as to platforms with human users.
(3) We developed a social network site with a user interface similar to existing services in order to perform experiments with human participants.
(4) We designed a first study in which we had both human participants as well as the previously mentioned bots interact on this social network site. The first study was fielded shortly after the end of the first reporting period.
(5) We worked on developing metrics to evaluate debate quality, both from a theoretical/normative as an empirical point of view.
(6) We developed ethical guidelines for the use, design, and deployment of digital twins of online social networks.
