  Social media provides many opportunities to monitor and evaluate political
phenomena such as referendums and elections. In this study, we propose a set of
approaches to analyze long-running political events on social media with a
real-world experiment: the debate about Brexit, i.e., the process through which
the United Kingdom activated the option of leaving the European Union. We
address the following research questions: Could Twitter-based stance
classification be used to demonstrate public stance with respect to political
events? What is the most efficient and comprehensive approach to measuring the
impact of politicians on social media? Which of the polarized sides of the
debate is more responsive to politician messages and the main issues of the
Brexit process? What is the share of bot accounts in the Brexit discussion and
which side are they for? By combining the user stance classification, topic
discovery, sentiment analysis, and bot detection, we show that it is possible
to obtain useful insights about political phenomena from social media data. We
are able to detect relevant topics in the discussions, such as the demand for a
new referendum, and to understand the position of social media users with
respect to the different topics in the debate. Our comparative and temporal
analysis of political accounts can detect the critical periods of the Brexit
process and the impact they have on the debate.
