Since the rise of social networks, daily exposure to fellow users’ opinions and comments has become a constant in many people’s lives. Research has shown the impact of even the most passive type of online user engagement, namely reading comments under a news item, can affect the perceived quality of the article, as well as change users’ perception of the public opinion of a certain issue. In order to automatically detect the reasoning underlying users’ opinions on political issues of public interest, this project aims to research a new methodology for argumentation mining of social media data in Dutch and English. We propose a novel framework and methodology to automatically extract topics, stance and argument structures from (political) social media data. A varied social media corpus will be collected and annotated. The insights we will gain from our pilot annotation study will be generalized to inform our novel argumentation theory, covering both linguistic characteristics of argumentative expressions as well as aspects from our extensive investigation into existing argumentation theories. It will form the basis of our novel argumentation annotation scheme which will be applied to the final corpus to train a system to automatically extract topics, stance and argumentative components and relations from our corpus. The insights of our research will help communication scientists and social scientists to formulate new hypotheses regarding the evolution of digital politics.