Combining deep learning and argumentative reasoning for the analysis of social media textual content using small datasets

Abstract

Deep learning has recently received a great deal of attention and has proven successful in a number of domains, including natural language understanding and computer vision, but it typically requires very large datasets. We define a deep learning method for Relation-based Argument Mining to extract argumentative relations of attack and support. We then use this method for determining whether news articles support tweets as well as for extracting Bipolar Argumentation Frameworks from reviews to help detect whether they are deceptive. We show experimentally that our method performs well in both settings. In particular, in the case of deception detection, our method contributes a novel argumentative feature that, when used in combination with other features in standard supervised classifiers, outperforms the latter even on small datasets.