The exponential rise of online social media has enabled the creation, distribution, and consumption of information at an unprecedented rate. However, it has also led to the burgeoning of various forms of online abuse. Increasing cases of online antisemitism have become one of the major concerns because of its socio-political consequences. Unlike other major forms of online abuse like racism, sexism, etc., online antisemitism has not been studied much from a machine learning perspective. To the best of our knowledge, we present the first work in the direction of automated multimodal detection of online antisemitism. The task poses multiple challenges that include extracting signals across multiple modalities, contextual references, and handling multiple aspects of antisemitism. Unfortunately, there does not exist any publicly available benchmark corpus for this critical task. Hence, we collect and label two datasets with 3,102 and 3,509 social media posts from Twitter and Gab respectively. Further, we present a multimodal deep learning system that detects the presence of antisemitic content and its specific antisemitism category using text and images from posts. We perform an extensive set of experiments on the two datasets to evaluate the efficacy of the proposed system. Finally, we also present a qualitative analysis of our study.
翻译:在线社交媒体的激增使得信息以前所未有的速度创建、传播和消费了前所未有的信息。然而,它也导致了各种形式的在线虐待的出现。越来越多的在线反犹太主义案例因其社会政治后果而成为主要关注问题之一。与其他主要形式的在线虐待形式不同,如种族主义、性别歧视等,在线反犹太主义没有从机器学习的角度做很多研究。我们最了解的是,我们展示了在自动多式联运检测在线反犹太主义方面开展的首项工作。这项任务提出了多重挑战,包括从多种模式、背景参考文献中提取信号,以及处理反犹太主义的多个方面。不幸的是,没有为这一关键任务建立任何可公开使用的基准资料库。因此,我们收集了两个数据集,分别有3,102个和3,509个来自Twitter和Gab的社交媒体文章。此外,我们展示了一个多式深度学习系统,用来检测反犹内容的存在及其使用文章和图像的具体反犹主义类别。我们还对两个数据库进行了广泛的实验,以评价拟议系统的效率。我们最后对两个数据库进行了定性分析。