进入战场：在线讨论中社区内冲突的量化和建模
Into the Battlefield: Quantifying and Modeling Intra-community Conflicts in Online Discussion
摘要：
在这项工作中，我们提出了一种新的量化冲突在线讨论。与以往的研究冲突动态，模型冲突作为一个二元现象，我们的措施是连续值，我们验证手动注释评级。我们提出了一个双向预测任务。首先，我们预测新闻文章可能面临的冲突程度从它的观众。我们使用多个机器学习框架来完成这项任务，使用从新闻文章中提取的各种特征。其次，给定一对用户及其交互历史，我们预测他们未来的参与是否会导致冲突。我们使用支持向量机将文本和基于网络的特征融合在一起，该支持向量机的AUC为0.89。此外，我们实现了一个图卷积模型，该模型利用用户的参与历史来预测一对以前从未见过的用户是否会有冲突的交互，AUC为0.69。我们对从Reddit新闻社区抓取的大量讨论数据集进行了研究，其中包含超过41，000篇新闻文章和550万条评论。除了预测任务之外，我们的研究还提供了关于冲突动态的有趣见解-用户如何基于冲突参与形成集群，不同在线新闻论坛上冲突的时间性质有多大差异，不同语言特征的贡献如何诱发冲突等。我们的研究为探索和模拟在线讨论社区内的冲突动态的新方法铺平了道路。
英文摘要：
In this work, we present a novel quantification of conflict in online discussion. Unlike previous studies on conflict dynamics, which model conflict as a binary phenomenon, our measure is continuous-valued, which we validate with manually annotated ratings. We address a two-way prediction task. Firstly, we predict the probable degree of conflict a news article will face from its audience. We employ multiple machine learning frameworks for this task using various features extracted from news articles. Secondly, given a pair of users and their interaction history, we predict if their future engagement will result in a conflict. We fuse textual and network-based features together using a support vector machine which achieves an AUC of 0.89. Moreover, we implement a graph convolutional model which exploits engagement histories of users to predict whether a pair of users who never met each other before will have a conflicting interaction, with an AUC of 0.69. We perform our studies on a massive discussion dataset crawled from the Reddit news community, containing over 41k news articles and 5.5 million comments. Apart from the prediction tasks, our studies offer interesting insights on the conflict dynamics -- how users form clusters based on conflicting engagements, how different is the temporal nature of conflict over different online news forums, how is contribution of different language based features to induce conflict, etc. In short, our study paves the way towards new methods of exploration and modeling of conflict dynamics inside online discussion communities.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
