Internet memes have emerged as an increasingly popular means of communication on the Web. Although typically intended to elicit humour, they have been increasingly used to spread hatred, trolling, and cyberbullying, as well as to target specific individuals, communities, or society on political, socio-cultural, and psychological grounds. While previous work has focused on detecting harmful, hateful, and offensive memes, identifying whom they attack remains a challenging and underexplored area. Here we aim to bridge this gap. In particular, we create a dataset where we annotate each meme with its victim(s) such as the name of the targeted person(s), organization(s), and community(ies). We then propose DISARM (Detecting vIctimS targeted by hARmful Memes), a framework that uses named entity recognition and person identification to detect all entities a meme is referring to, and then, incorporates a novel contextualized multimodal deep neural network to classify whether the meme intends to harm these entities. We perform several systematic experiments on three test setups, corresponding to entities that are (a) all seen while training, (b) not seen as a harmful target on training, and (c) not seen at all on training. The evaluation results show that DISARM significantly outperforms ten unimodal and multimodal systems. Finally, we show that DISARM is interpretable and comparatively more generalizable and that it can reduce the relative error rate for harmful target identification by up to 9 points absolute over several strong multimodal rivals.
翻译:互联网中位器已成为网络上日益流行的通信手段,虽然通常意在引起幽默,但通常都被用来散布仇恨、煽动和网络欺凌,以及以政治、社会文化和心理理由针对特定个人、社区或社会,尽管以前的工作重点是发现有害、仇恨和攻击性迷子,确定他们攻击的对象仍然是具有挑战性和探索不足的领域,这里我们的目标是缩小这一差距。特别是,我们制作了一个数据集,我们用该数据集向每个被害者(如被害者)、组织和社区(如被害者、组织和社区的名称)作笔记。我们然后提议DISARM(检测受人、社会文化和心理因素攻击的VICIMS),这个框架使用名称实体识别和识别人名来侦测所有被攻击的实体,然后采用一个新的背景化的多式深度神经网络来分类他们是否打算伤害这些实体。我们在三种竞争组合上进行了几次系统的系统实验,这三者都是(a)所看到的所有被害者的姓名,(b)在培训中的相对比例上,我们并没有看到一个以有害性指标为最后,我们所看到的是有害性标准。