WikiConv: A Corpus of the Complete Conversational History of a Large Online Collaborative Community
WikiConv: A Corpus of the Complete Conversational History of a Large Online Collaborative Community
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DOI：
10.18653/v1/d18-1305
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发表时间：
2018-10
期刊：
影响因子：
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通讯作者：
Yiqing Hua;Cristian Danescu-Niculescu-Mizil;Dario Taraborelli;Nithum Thain;Jeff Sorensen;Lucas Dixon
中科院分区：
文献类型：
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作者：
Yiqing Hua;Cristian Danescu-Niculescu-Mizil;Dario Taraborelli;Nithum Thain;Jeff Sorensen;Lucas Dixon
我们提出了一个语料库，包括贡献者之间的对话维基百科，最大的在线协作社区之一的完整历史。通过记录对话的中间状态--不仅包括评论和回复，还包括它们的修改、删除和删除--这些数据为在线对话提供了前所未有的视角。我们的框架被设计为语言不可知的，我们表明，它提取高质量的数据在中文和英文。这种详细程度支持与大规模在线协作的过程（和挑战）有关的新研究问题。我们说明了语料库的潜力与两个案例研究的英文维基百科，突出了新的角度对早期的工作。首先，我们探讨一个人的会话行为如何取决于他们如何与讨论的地点。其次，我们表明，社区节制有毒行为发生的速度比以前估计的要高。
We present a corpus that encompasses the complete history of conversations between contributors to Wikipedia, one of the largest online collaborative communities. By recording the intermediate states of conversations - including not only comments and replies, but also their modifications, deletions and restorations - this data offers an unprecedented view of online conversation. Our framework is designed to be language agnostic, and we show that it extracts high quality data in both Chinese and English. This level of detail supports new research questions pertaining to the process (and challenges) of large-scale online collaboration. We illustrate the corpus’ potential with two case studies on English Wikipedia that highlight new perspectives on earlier work. First, we explore how a person’s conversational behavior depends on how they relate to the discussion’s venue. Second, we show that community moderation of toxic behavior happens at a higher rate than previously estimated.