论文标题
网络欺凌检测的概括
Generalisation of Cyberbullying Detection
论文作者
论文摘要
在当今无处不在的在线社区中，网络欺凌是一个问题。从在线对话中过滤它已证明是一个挑战，并且努力导致创建了许多不同的数据集，所有这些数据集都作为培训分类器的资源提供。通过这些数据集，我们将探讨网络欺凌行为的各种定义，以及这些差异对一个分类器对另一个社区的可移植性的影响。通过分析数据集之间的相似性，我们还可以洞悉受其训练的分类器的概括能力。对结合这些分类器的合奏模型的研究将有助于我们了解它们之间的相互作用。
Cyberbullying is a problem in today's ubiquitous online communities. Filtering it out of online conversations has proven a challenge, and efforts have led to the creation of many different datasets, all offered as resources to train classifiers. Through these datasets, we will explore the variety of definitions of cyberbullying behaviors and the impact of these differences on the portability of one classifier to another community. By analyzing the similarities between datasets, we also gain insight on the generalization power of the classifiers trained from them. A study of ensemble models combining these classifiers will help us understand how they interact with each other.
