论文标题
恨我不是：检测仇恨诱导代码转换语言中的模因
Hate Me Not: Detecting Hate Inducing Memes in Code Switched Languages
论文作者
论文摘要
社交媒体用户数量的增加导致在线发布的可恨内容的增加。在讲多种语言的印度等国家，这些憎恶的帖子来自不寻常的代码切换语言。这种仇恨言论是在图像的帮助下描绘的，以形成“模因”，从而对人类的思想产生持久的影响。在本文中，我们从多模式数据（即包含代码切换语言的文本的图像（模因）中掌握了仇恨和犯罪检测的任务。我们首先提出了一个新颖的三个注释的印度政治模因（IPM）数据集，该数据集包括来自印度各种政治事件的模因，这些模因在独立后发生，并被归类为三个不同的类别。我们还提出了一个基于二进制的CNN CNN和LSTM模型，以使用CNN模型和文本使用LSTM模型来处理图像，以获得此任务的最新结果。
The rise in the number of social media users has led to an increase in the hateful content posted online. In countries like India, where multiple languages are spoken, these abhorrent posts are from an unusual blend of code-switched languages. This hate speech is depicted with the help of images to form "Memes" which create a long-lasting impact on the human mind. In this paper, we take up the task of hate and offense detection from multimodal data, i.e. images (Memes) that contain text in code-switched languages. We firstly present a novel triply annotated Indian political Memes (IPM) dataset, which comprises memes from various Indian political events that have taken place post-independence and are classified into three distinct categories. We also propose a binary-channelled CNN cum LSTM based model to process the images using the CNN model and text using the LSTM model to get state-of-the-art results for this task.
