论文标题
Henin：学习在社交媒体上可解释的网络欺凌检测的异质神经相互作用网络
HENIN: Learning Heterogeneous Neural Interaction Networks for Explainable Cyberbullying Detection on Social Media
论文作者
论文摘要
在对网络欺凌的计算检测中，现有工作主要集中在构建一般的分类器上，这些分类器仅依赖于社交媒体会议的文本分析。尽管取得了经验的成功，但我们认为，重要的缺失作品是模型解释性，即为什么将特定的媒体会话视为网络欺凌。因此，在本文中，我们提出了一种新型的深层模型，异质的神经相互作用网络（HENIN），以解释网络欺凌检测。 Henin包含以下组成部分：评论编码器，征询后共同注意子网以及会话 - 会议和固定后的互动提取器。在真实数据集上进行的广泛实验不仅表现出Henin的有希望的表现，而且还强调了证据评论，因此可以理解为什么将媒体会话确定为网络欺凌。
In the computational detection of cyberbullying, existing work largely focused on building generic classifiers that rely exclusively on text analysis of social media sessions. Despite their empirical success, we argue that a critical missing piece is the model explainability, i.e., why a particular piece of media session is detected as cyberbullying. In this paper, therefore, we propose a novel deep model, HEterogeneous Neural Interaction Networks (HENIN), for explainable cyberbullying detection. HENIN contains the following components: a comment encoder, a post-comment co-attention sub-network, and session-session and post-post interaction extractors. Extensive experiments conducted on real datasets exhibit not only the promising performance of HENIN, but also highlight evidential comments so that one can understand why a media session is identified as cyberbullying.
