  Argumentation mining is a rising subject in the computational linguistics
domain focusing on extracting structured arguments from natural text, often
from unstructured or noisy text. The initial approaches on modeling arguments
was aiming to identify a flawless argument on specific fields (Law, Scientific
Papers) serving specific needs (completeness, effectiveness). 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. In this survey article, we
bridge the gap between theoretical approaches of argumentation mining and
pragmatic schemes that satisfy the needs of social media generated data,
recognizing the need for adapting more flexible and expandable schemes, capable
to adjust to the argumentation conditions that exist in social media. We
review, compare, and classify existing approaches, techniques and tools,
identifying the positive outcome of combining tasks and features, and
eventually propose a conceptual architecture framework. The proposed
theoretical framework is an argumentation mining scheme able to identify the
distinct sub-tasks and capture the needs of social media text, revealing the
need for adopting more flexible and extensible frameworks.
