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Argument mining has recently emerged as a promising field at the frontiers of the argumentation and text mining communities. However, most techniques developed within that field do not scale to larger amounts of data, depriving us for example of valuable insights in large-scale discussion forums. On two social media datasets, we study different lightweight scalable text mining techniques used within the sentiment analysis community and their applicability to the argument mining problem.