Something’s Brewing! Early Prediction of Controversy-causing Posts from Discussion Features
Something’s Brewing! Early Prediction of Controversy-causing Posts from Discussion Features
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DOI：
10.18653/v1/n19-1166
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发表时间：
2019-04
期刊：
影响因子：
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通讯作者：
Jack Hessel;Lillian Lee
中科院分区：
文献类型：
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作者：
Jack Hessel;Lillian Lee
有争议的帖子是指那些分裂社区偏好的帖子，同时收到显着的正面反馈和显着的负面反馈。我们在这里加入“社区”一词是经过深思熟虑的：对某些受众有争议的内容对其他人来说可能并非如此。使用来自 reddit.com 上几个不同社区的数据，我们利用从文本内容和发起讨论的早期评论的树结构中提取的特征来预测帖子的最终争议性。我们发现，即使只有少数评论可用，例如，原始帖子 15 分钟内发表的前 5 条评论，讨论功能通常也会为强大的内容和评分基线添加预测能力。关于域转移的其他实验表明，对话结构特征通常比对话内容特征更好地推广到其他社区。
Controversial posts are those that split the preferences of a community, receiving both significant positive and significant negative feedback. Our inclusion of the word “community” here is deliberate: what is controversial to some audiences may not be so to others. Using data from several different communities on reddit.com, we predict the ultimate controversiality of posts, leveraging features drawn from both the textual content and the tree structure of the early comments that initiate the discussion. We find that even when only a handful of comments are available, e.g., the first 5 comments made within 15 minutes of the original post, discussion features often add predictive capacity to strong content-and- rate only baselines. Additional experiments on domain transfer suggest that conversation- structure features often generalize to other communities better than conversation-content features do.