This work is based on the submission to the competition Hindi Constraint conducted by AAAI@2021 for detection of hostile posts in Hindi on social media platforms. Here, a model is presented for detection and classification of hostile posts and further classify into fake, offensive, hate and defamation using Relational Graph Convolutional Networks. Unlike other existing work, our approach is focused on using semantic meaning along with contextutal information for better classification. The results from AAAI@2021 indicates that the proposed model is performing at par with Google's XLM-RoBERTa on the given dataset. Our best submission with RGCN achieves an F1 score of 0.97 (7th Rank) on coarse-grained evaluation and achieved best performance on identifying fake posts. Among all submissions to the challenge, our classification system with XLM-Roberta secured 2nd rank on fine-grained classification.

翻译：这项工作的基础是AAAI@2021为在社交媒体平台上检测印地语敌对职位而向竞争的印地语控制局提交的呈文。这里展示了一个模型,用于检测和分类敌对职位,并使用关系图革命网络进一步归类为假的、攻击性的、仇恨的和诽谤。与其他现有工作不同的是,我们的方法侧重于使用语义意义和背景信息,以更好地分类。AAAI@2021的结果表明,拟议模式与谷歌的XLM-ROBERTA在给定数据集上的表现相同。我们与RGCN的最佳呈文在粗略评价方面达到了0.97分(第7级)的F1分,并在识别假职位方面取得了最佳业绩。在所有对挑战的呈文中,我们的XLM-Roberta分类系统在精细分类上以XLM-Roberta为第二级。