论文标题
检测实体在有害模因中的作用：技术及其局限性
Detecting the Role of an Entity in Harmful Memes: Techniques and Their Limitations
论文作者
论文摘要
随着时间的流逝，有害或虐待的在线内容一直在增加，引起了对社交媒体平台，政府机构和政策制定者的担忧。这种有害或虐待的内容可能会对社会产生重大的负面影响，例如，网络欺凌会导致自杀，关于COVID-19的谣言可能会引起疫苗犹豫，促进伪造的治疗方法对COVID-19造成健康危害和死亡。在线发布和共享的内容可以是文本，视觉或两者的组合，例如在模因中。在这里，我们描述了我们在有害模因中检测实体（英雄，小人，受害者）角色的实验，这是约束-2022共享任务的一部分，以及我们执行任务的系统。我们进一步提供了不同的实验环境（即单峰，多模式，注意力和增强）的比较分析。为了重现性，我们可以公开使用实验代码。 \ url {https://github.com/robi56/harmful_memes_block_fusion}
Harmful or abusive online content has been increasing over time, raising concerns for social media platforms, government agencies, and policymakers. Such harmful or abusive content can have major negative impact on society, e.g., cyberbullying can lead to suicides, rumors about COVID-19 can cause vaccine hesitance, promotion of fake cures for COVID-19 can cause health harms and deaths. The content that is posted and shared online can be textual, visual, or a combination of both, e.g., in a meme. Here, we describe our experiments in detecting the roles of the entities (hero, villain, victim) in harmful memes, which is part of the CONSTRAINT-2022 shared task, as well as our system for the task. We further provide a comparative analysis of different experimental settings (i.e., unimodal, multimodal, attention, and augmentation). For reproducibility, we make our experimental code publicly available. \url{https://github.com/robi56/harmful_memes_block_fusion}
