论文标题
通过平台感知的对抗性编码，社交媒体平台之间的网络欺凌检测
Cyberbullying detection across social media platforms via platform-aware adversarial encoding
论文作者
论文摘要
尽管对网络欺凌检测的兴趣越来越高，但现有的努力在很大程度上仅限于在一个平台上的实验，并且它们在不同社交媒体平台上的普遍性受到了较少的关注。我们提出了XP-CB，这是一种基于变压器和对抗性学习的新型跨平台框架。 XP-CB可以增强从源和目标平台中利用未标记数据的变压器，以提出共同的表示，同时预防特定于平台的培训。为了验证我们提出的框架，我们通过六个跨平台配置从三个不同平台进行网络欺凌数据集进行了实验，显示了其在Bert和Roberta作为基础变压器模型的有效性。
Despite the increasing interest in cyberbullying detection, existing efforts have largely been limited to experiments on a single platform and their generalisability across different social media platforms have received less attention. We propose XP-CB, a novel cross-platform framework based on Transformers and adversarial learning. XP-CB can enhance a Transformer leveraging unlabelled data from the source and target platforms to come up with a common representation while preventing platform-specific training. To validate our proposed framework, we experiment on cyberbullying datasets from three different platforms through six cross-platform configurations, showing its effectiveness with both BERT and RoBERTa as the underlying Transformer models.
