  With the growing interest in social applications of Natural Language
Processing and Computational Argumentation, a natural question is how
controversial a given concept is. Prior works relied on Wikipedia's metadata
and on content analysis of the articles pertaining to a concept in question.
Here we show that the immediate textual context of a concept is strongly
indicative of this property, and, using simple and language-independent
machine-learning tools, we leverage this observation to achieve
state-of-the-art results in controversiality prediction. In addition, we
analyze and make available a new dataset of concepts labeled for
controversiality. It is significantly larger than existing datasets, and grades
concepts on a 0-10 scale, rather than treating controversiality as a binary
label.
