Every complex system that involves two different kinds of agents interacting with each other can be described as a bipartite network, whose analysis can give new information about the system itself. Examples of these kind of systems can be found in many different areas of science, such as ecology, economics, social science. However, every graph can be remapped to a bipartite nodes-links graph. As this kind of framework gains more and more attention, we take a look at the bipartite null models and projection methods that exist, and how to deal with networks of large size and density. We focus our attention on a case study of the Twitter debate on Brexit during the UK elections of 2019, where we are able to build several (bipartite) networks of interactions between users and to characterize the presence and activity of automated accounts. Among the results of this study, we find that malicious users are injected in the debate at crucial times, that there is a class of suspicious users which are able not to be suspended by Twitter by maintaining a low profile, and that there are bots polluting the Brexit discourse with other populist topics. Useful references: Bruno et al., Brexit and bots: characterizing the behaviour of automated accounts on Twitter during the UK election, 2022 Vallarano et al., Fast and scalable likelihood maximization for Exponential Random Graph Models with local constraints, 2021 Saracco et al., Inferring monopartite projections of bipartite networks: an entropy-based approach, 2017 Python package: https://github.com/mat701/BiCM