  An ability to infer the political leaning of social media users can help in
gathering opinion polls thereby leading to a better understanding of public
opinion. While there has been a body of research attempting to infer the
political leaning of social media users, this has been typically simplified as
a binary classification problem (e.g. left vs right) and has been limited to a
single location, leading to a dearth of investigation into more complex,
multiclass classification and its generalizability to different locations,
particularly those with multi-party systems. Our work performs the first such
effort by studying political leaning inference in three of the UK's nations
(Scotland, Wales and Northern Ireland), each of which has a different political
landscape composed of multiple parties. To do so, we collect and release a
dataset comprising users labelled by their political leaning as well as
interactions with one another. We investigate the ability to predict the
political leaning of users by leveraging these interactions in challenging
scenarios such as few-shot learning, where training data is scarce, as well as
assessing the applicability to users with different levels of political
engagement. We show that interactions in the form of retweets between users can
be a very powerful feature to enable political leaning inference, leading to
consistent and robust results across different regions with multi-party
systems. However, we also see that there is room for improvement in predicting
the political leaning of users who are less engaged in politics.
