论文标题
调查社交媒体中仇恨言论检测的深度学习方法
Investigating Deep Learning Approaches for Hate Speech Detection in Social Media
论文作者
论文摘要
互联网上的惊人增长有助于赋予个人表达的能力，但是滥用言论自由也导致了各种网络犯罪和反社会活动的增加。仇恨言论是一个这样的问题，需要非常重视其他问题，这可能会对社交面料的完整性构成威胁。 在本文中，我们提出了使用各种嵌入的深度学习方法来检测社交媒体中的各种仇恨言论。从大量文本中检测仇恨言论，尤其是包含有限上下文信息的推文也带来了一些实际的挑战。 此外，用户生成的数据和各种形式的仇恨言论的存在使确定消息的程度和意图非常具有挑战性。我们在三个不同域的公开数据集上进行的实验显示了准确性和F1得分的显着提高。
The phenomenal growth on the internet has helped in empowering individual's expressions, but the misuse of freedom of expression has also led to the increase of various cyber crimes and anti-social activities. Hate speech is one such issue that needs to be addressed very seriously as otherwise, this could pose threats to the integrity of the social fabrics. In this paper, we proposed deep learning approaches utilizing various embeddings for detecting various types of hate speeches in social media. Detecting hate speech from a large volume of text, especially tweets which contains limited contextual information also poses several practical challenges. Moreover, the varieties in user-generated data and the presence of various forms of hate speech makes it very challenging to identify the degree and intention of the message. Our experiments on three publicly available datasets of different domains shows a significant improvement in accuracy and F1-score.
