We present a large annotated corpus of scholarly discourse in the domain of International Relations, a subfield of political science. The corpus comprises 190 articles (over 1500K tokens) annotated at the argumentation, basic rhetorical, and domain level. Five of the included articles (ca. 62K tokens) constitute a Gold-standard, coded by domain experts. The remaining articles were coded by annotators trained on the Gold-standard and monitored for annotation quality. We describe our corpus creation methodology, the annotation process and quality assurance, the corpus itself, and present insights into the data: Most argumentative structures in the data are simple premise-conclusion structures, fewer than half of the claims have explicit supporting evidence. Counter-arguments to claims are rare. The claim-to-support ratio varies widely between articles; possibly to some extent due to the topics covered (with clear common ground) or to the differences between authors’ styles. The distribution of theoretical vs. evaluative statements varies strongly between articles; this can be attributed to such factors as different methodological approaches between the articles and the methodological focus of the publishing journal.

In times of fake news and alternative facts, pro and con arguments on controversial topics are of increasing importance. Recently, we presented args.me as the first search engine for arguments on the web. In its initial version, args.me ranked arguments solely by their relevance to a topic queried for, making it hard to learn about the diverse topical aspects covered by the search results. To tackle this shortcoming, we integrated a visualization interface for result exploration in args.me that provides an instant overview of the main aspects in a barycentric coordinate system. This topic space is generated ad-hoc from controversial issues on Wikipedia and argument-specific LDA models. In two case studies, we demonstrate how individual arguments can be found easily through interactions with the visualization, such as highlighting and filtering.