The internet and social media have been a huge force for change in our society, transforming the way we communicate and seek information. However, the unprecedented volumes of data have created a unique set of challenges: misinformation, polarization and online conflict. Misinformation can be particularly detrimental to society, democracy and public health. As a result, there is a growing area of research into countermeasures against online misinformation. However, much of this research is conducted in small scale experiments and cannot predict the macro-level impact. This paper demonstrates that agent-based modelling can be a useful tool for policy-makers to evaluate these countermeasures at scale before implementing them on a social media platform. This research has the following contributions: (i) Development of an agent-based model of the spread of information on a social media network, based on Twitter. (ii) Calibration and validation of the proposed model by Twitter data following fake and true news stories. (iii) Using agent-based modelling to evaluate the impact of countermeasures on the spread of fake news and on general information sharing.