Argumentation mining (AM) is a rising subject in the computational linguistics domain focusing on extracting structured arguments from natural text, often from unstructured or noisy text. With the emerge of Web 2.0 and the explosion in the use of Social Media both the diffusion of the data and the argument structure have changed. The supervised machine learning (ML) techniques that have been widely applied seem to have reached their plateau, thus the state-of-the-art research is focusing on ways of exploiting unsupervised ML techniques. This study is an effort on bridging the gap between theoretical approaches of argumentation mining and pragmatic schemes, techniques and algorithms that satisfy the needs of AM in Web-generated data.
