In online debates, as in offline ones, individual utterances or arguments support or attack each other, leading to some subset of arguments (potentially from different sides of the debate) being considered more relevant than others. However, online conversations are much larger in scale than offline ones, with often hundreds of thousands of users weighing in, collaboratively forming large trees of comments by starting from an original post and replying to each other. In large discussions, readers are often forced to sample a subset of the arguments being put forth. Since such sampling is rarely done in a principled manner, users may not read all the relevant arguments to get a full picture of the debate from a sample. This article is interested in answering the question of how users should sample online conversations to selectively favour the currently justified or accepted positions in the debate. We apply techniques from argumentation theory and complex networks to build a model that predicts the probabilities of the normatively justified arguments given their location in idealised online discussions of comments and replies, which we represent as trees. Our model shows that the proportion of replies that are supportive, the distribution of the number of replies that comments receive, and the locations of comments that do not receive replies (i.e., the “leaves” of the reply tree) all determine the probability that a comment is a justified argument given its location. We show that when the distribution of the number of replies is homogeneous along the tree length, for acrimonious discussions (with more attacking comments than supportive ones), the distribution of justified arguments depends on the parity of the tree level, which is the distance from the root expressed as number of edges. In supportive discussions, which have more supportive comments than attacks, the probability of having justified comments increases as one moves away from the root. For discussion trees that have a non-homogeneous in-degree distribution, for supportive discussions we observe the same behaviour as before, while for acrimonious discussions we cannot observe the same parity-based distribution. This is verified with data obtained from the online debating platform Kialo. By predicting the locations of the justified arguments in reply trees, we can therefore suggest which arguments readers should sample, to grasp the currently accepted opinions in such discussions. Our models have important implications for the design of future online debating platforms.

中文翻译：

---

谁说了算？了解如何对在线讨论进行抽样

在在线辩论中，与在离线辩论中一样，个别话语或论点相互支持或攻击，导致某些论点子集（可能来自辩论的不同方面）被认为比其他论点更相关。然而，在线对话的规模远大于线下对话，通常有数十万用户参与进来，通过从原始帖子开始并相互回复，协作形成大量评论树。在大型讨论中，读者经常被迫对所提出的论点的一个子集进行抽样。由于此类抽样很少以有原则的方式进行，因此用户可能无法阅读所有相关论据以从样本中全面了解辩论。本文有兴趣回答用户应如何对在线对话进行抽样以有选择地支持当前在辩论中合理或接受的立场。我们应用论证理论和复杂网络中的技术来构建一个模型，该模型可以预测规范合理论证的概率，因为它们在理想化的在线评论和回复讨论中的位置，我们将其表示为树。我们的模型表明，支持的回复比例、评论收到的回复数量的分布以及未收到回复的评论的位置（即回复树的“叶子”）都决定了鉴于其位置，评论是一个合理的论点。我们表明，当回复数量的分布沿树长度均匀分布时，对于激烈的讨论（攻击性评论多于支持性评论），合理论证的分布取决于树级别的奇偶性，即距离根表示为边数。在支持性讨论中，支持性评论多于攻击，随着人们远离根源，获得合理评论的可能性会增加。对于具有非均匀度数分布的讨论树，对于支持性讨论，我们观察到与以前相同的行为，而对于激烈的讨论，我们不能观察到相同的基于奇偶性的分布。这已通过从在线辩论平台 Kialo 获得的数据进行了验证。通过预测回复树中合理论点的位置，我们可以建议读者应该采样哪些论点，以掌握当前在此类讨论中接受的意见。我们的模型对未来在线辩论平台的设计具有重要意义。