Over the years, the number of users of social media has increased drastically. People frequently share their thoughts through social platforms, and this leads to an increase in hate content. In this virtual community, individuals share their views, express their feelings, and post photos, videos, blogs, and more. Social networking sites like Facebook and Twitter provide platforms to share vast amounts of content with a single click. However, these platforms do not impose restrictions on the uploaded content, which may include abusive language and explicit images unsuitable for social media. To resolve this issue, a new idea must be implemented to divide the inappropriate content. Numerous studies have been done to automate the process. In this paper, we propose a new Bi-GRU-CNN model to classify whether the text is offensive or not. The combination of the Bi-GRU and CNN models outperforms the existing model.

翻译：近年来，社交媒体用户数量急剧增长。人们频繁通过社交平台分享观点，这导致仇恨内容的增加。在这个虚拟社区中，个体分享观点、表达情感，并发布照片、视频、博客等内容。Facebook和Twitter等社交网站提供了通过单次点击即可分享海量内容的平台。然而，这些平台对上传内容缺乏限制，其中可能包含辱骂性语言和不适于社交媒体的露骨图像。为解决这一问题，需要实施新的方案来区分不当内容。已有大量研究致力于实现该过程的自动化。本文提出了一种新型Bi-GRU-CNN模型，用于对文本是否具有攻击性进行分类。双向门控循环单元与CNN模型的组合在性能上超越了现有模型。