Argotario: Computational Argumentation Meets Serious Games
This work addresses a gap for argumentation scholars and NLP researchers by providing a scalable data acquisition method for fallacies, though it is incremental as it applies an existing serious games methodology to a new domain.
The paper tackles the lack of empirical resources for studying fallacies in argumentation by developing Argotario, a serious game that collects and annotates fallacious arguments, resulting in a multilingual, open-source tool accessible online.
An important skill in critical thinking and argumentation is the ability to spot and recognize fallacies. Fallacious arguments, omnipresent in argumentative discourse, can be deceptive, manipulative, or simply leading to `wrong moves' in a discussion. Despite their importance, argumentation scholars and NLP researchers with focus on argumentation quality have not yet investigated fallacies empirically. The nonexistence of resources dealing with fallacious argumentation calls for scalable approaches to data acquisition and annotation, for which the serious games methodology offers an appealing, yet unexplored, alternative. We present Argotario, a serious game that deals with fallacies in everyday argumentation. Argotario is a multilingual, open-source, platform-independent application with strong educational aspects, accessible at www.argotario.net.