Constructing and evaluating bipolar weighted argumentation frameworks for online debating systems
Andrea Pazienza, Stefano Ferilli, Floriana Esposito · CINECA IRIS Institutional Research Information System (University of Bari Aldo Moro) · 2017
Discussions on social Web platforms carry a lot of information which is more and more difficult to analyze. Given a virtual community of users that discuss a particular topic of interest, an important task is to extract a model of the whole debate in order to automatically evaluate what are the most reliable claims. This paper proposes to approach this task using abstract argumentation, and define a new argument system, called Bipolar Weighted Argumentation Framework. It is able to capture all the useful information from a discussion thread, including the strength of positive (i.e., supports) and negative (i.e., attacks) relations between arguments. It also provides a way to assess an acceptability degree for each argument by means of the strength propagation of indirect relations ending to it, and a strategy to build such a framework from an online debate with a hierarchical structure. A model obtained from a real life discussion (a Reddit thread) is discussed and qualitatively evaluated.