  Controversy is widespread online. Previous studies mainly define controversy
based on vague assumptions of its relation to sentiment such as hate speech and
offensive words. This paper introduces the first question-answering dataset
that defines content controversy by user perception, i.e., votes from plenty of
users. It contains nearly 10K questions, and each question has a best answer
and a most controversial answer. Experimental results reveal that controversy
detection in question answering is essential and challenging, and there is no
strong correlation between controversy and sentiment tasks.
