论文标题
顶级评论还是翻牌评论？预测和解释在线新闻讨论中的用户参与度
Top Comment or Flop Comment? Predicting and Explaining User Engagement in Online News Discussions
论文作者
论文摘要
在线新闻文章下面的评论部分在读者中越来越受欢迎。但是，大量的评论使普通的新闻消费者阅读所有内容并阻碍讨论的讨论是不可行的。大多数平台以时间顺序显示评论，这忽略了其中一些与用户更相关的评论，并且是更好的对话启动器。在本文中，我们系统地以upvotes的形式系统地分析了用户参与，并回答了评论收到的内容。根据评论文本，我们训练一个模型来区分那些收到许多高投票和答复的机会的评论。我们对theguardian.com的用户评论的评估比较了经常性和卷积神经网络模型，以及传统的基于功能的分类器。此外，我们研究了什么使一些评论比其他评论更具吸引力。为此，我们确定参与触发器并将其安排在分类法中。神经网络的解释方法揭示了哪些输入词对我们的模型的预测具有最大的影响。此外，我们在产品评论的数据集上进行了评估，该数据集具有与用户评论相似的属性，例如以供应为有用的投票。
Comment sections below online news articles enjoy growing popularity among readers. However, the overwhelming number of comments makes it infeasible for the average news consumer to read all of them and hinders engaging discussions. Most platforms display comments in chronological order, which neglects that some of them are more relevant to users and are better conversation starters. In this paper, we systematically analyze user engagement in the form of the upvotes and replies that a comment receives. Based on comment texts, we train a model to distinguish comments that have either a high or low chance of receiving many upvotes and replies. Our evaluation on user comments from TheGuardian.com compares recurrent and convolutional neural network models, and a traditional feature-based classifier. Further, we investigate what makes some comments more engaging than others. To this end, we identify engagement triggers and arrange them in a taxonomy. Explanation methods for neural networks reveal which input words have the strongest influence on our model's predictions. In addition, we evaluate on a dataset of product reviews, which exhibit similar properties as user comments, such as featuring upvotes for helpfulness.
