论文标题
自动检测网络欺凌对妇女和移民以及跨域的适应性
Automated Detection of Cyberbullying Against Women and Immigrants and Cross-domain Adaptability
论文作者
论文摘要
由于社交媒体技术的激增，网络欺凌是一个普遍且不断增长的社会问题。少数民族，妇女和青少年是网络欺凌的普遍受害者。尽管NLP技术发展了进步，但自动化的网络欺凌检测仍然具有挑战性。本文着重于使用最先进的NLP技术推进技术。我们使用来自Semeval 2019的Twitter数据集 - 关于针对妇女和移民的仇恨言论的任务5（Hateval）。我们基于Distilbert的最佳性能合奏模型在分别分类仇恨言论（任务A），攻击性和目标（任务B）的任务中达到了0.73和0.74的F1分数。我们调整了为任务A开发的合奏模型，以对外部数据集中的进攻语言进行分类，并使用三个基准数据集在F1分数中实现了〜0.7，从而为跨域的适应性提供了有希望的结果。我们对错误分类的推文进行了定性分析，为将来的网络欺凌研究提供了有见地的建议。
Cyberbullying is a prevalent and growing social problem due to the surge of social media technology usage. Minorities, women, and adolescents are among the common victims of cyberbullying. Despite the advancement of NLP technologies, the automated cyberbullying detection remains challenging. This paper focuses on advancing the technology using state-of-the-art NLP techniques. We use a Twitter dataset from SemEval 2019 - Task 5(HatEval) on hate speech against women and immigrants. Our best performing ensemble model based on DistilBERT has achieved 0.73 and 0.74 of F1 score in the task of classifying hate speech (Task A) and aggressiveness and target (Task B) respectively. We adapt the ensemble model developed for Task A to classify offensive language in external datasets and achieved ~0.7 of F1 score using three benchmark datasets, enabling promising results for cross-domain adaptability. We conduct a qualitative analysis of misclassified tweets to provide insightful recommendations for future cyberbullying research.
