Robust Cyberbullying Detection with Causal Interpretation
Robust Cyberbullying Detection with Causal Interpretation
复制标题
具有因果解释的强大网络欺凌检测
DOI：
10.1145/3308560.3316503
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Huan Liu
中科院分区：
文献类型：
--
作者：
Lu Cheng;Ruocheng Guo;Huan Liu
网络欺凌对青春期前和青少年构成严重威胁，因此，了解网络欺凌背后的动机对于防止其发生并减轻影响至关重要。大多数现有的网络欺凌检测工作都集中在准确性上，而忽视了结果的原因。由于混杂因素的存在，从观察数据中发现网络欺凌的原因具有挑战性，这些混杂因素可能导致协变量和结果之间出现虚假因果关系。这项工作研究了具有因果解释的稳健网络欺凌检测问题，并提出了一个原则框架来识别和阻止可能的混杂因素（即 p 混杂因素）的影响。去混杂模型具有因果解释性，并且对于数据分布的变化更加稳健。我们使用最先进的评估方法——因果可迁移性来测试我们的方法。实验结果证实了我们提出的算法的有效性。本研究的目的是提供一种计算手段，从观察数据中了解网络欺凌行为。这提高了我们预测和促进有效战略或政策的能力，以主动减轻网络欺凌的影响。
Cyberbullying poses serious threats to preteens and teenagers, therefore, understanding the incentives behind cyberbullying is critical to prevent its happening and mitigate the impact. Most existing work towards cyberbullying detection has focused on the accuracy, and overlooked causes of the outcome. Discovering the causes of cyberbullying from observational data is challenging due to the existence of confounders, variables that can lead to spurious causal relationships between covariates and the outcome. This work studies the problem of robust cyberbullying detection with causal interpretation and proposes a principled framework to identify and block the influence of the plausible confounders, i.e., p-confounders. The de-confounded model is causally interpretable and is more robust to the changes in data distribution. We test our approach using the state-of-the-art evaluation method, causal transportability. The experimental results corroborate the effectiveness of our proposed algorithm. The purpose of this study is to provide a computational means to understanding cyberbullying behavior from observational data. This improves our ability to predict and to facilitate effective strategies or policies to proactively mitigate the impact of cyberbullying.