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.

翻译：争议性在网络上普遍存在。以往的研究主要基于争议性与仇恨言论、冒犯性词汇等情感因素之间的模糊假设来定义争议。本文首次引入了一个通过用户感知（即大量用户的投票）来定义内容争议性的问答数据集。该数据集包含近10,000个问题，每个问题均有一个最佳回答和一个最具争议性的回答。实验结果表明，问答中的争议性检测至关重要且具有挑战性，且争议性与情感任务之间并无强相关性。