The recommendation algorithms on social media platforms are hugely impactful,
they shape information flow and human connection on an unprecedented scale.
Despite growing criticism of the social impact of these algorithms, they are still
opaque and transparency is an ongoing challenge. This paper has three contri butions: (1) We introduce the concept of sociotechnical transparency. This can
be defined as transparency approaches that consider both the technical system,
and how it interacts with users and the environment in which it is deployed.
We propose sociotechnical approaches to improve understanding of social media
algorithms for policy-makers and the public. (2) We present an approach to
sociotechnical transparency using agent-based modelling, which overcomes a
number of challenges with existing approaches. This is a novel application of
agent-based modelling to provide transparency into how the recommendation
algorithm prioritises different curation signals for a topic. (3) This agent-based
model has a novel implementation of a multi-objective recommendation algorithm
that is calibrated and empirically validated with data collected from X, previ ously Twitter. We show that agent-based modelling can provide useful insights
into how the recommendation algorithm prioritises different curation signals. We
can begin to explore whether the priorities of the recommendation algorithm
align with what platforms say it is doing and whether they align with what the
public want.