Target-based Stance Detection is the task of finding a stance toward a target. Twitter is one of the primary sources of political discussions in social media and one of the best resources to analyze Stance toward entities. This work proposes a new method toward Target-based Stance detection by using the stance of replies toward a most important and arguing target in source tweet. This target is detected with respect to the source tweet itself and not limited to a set of pre-defined targets which is the usual approach of the current state-of-the-art methods. Our proposed new attitude resulted in a new corpus called ExaASC for the Arabic Language, one of the low resource languages in this field. In the end, we used BERT to evaluate our corpus and reached a 70.69 Macro F-score. This shows that our data and model can work in a general Target-base Stance Detection system. The corpus is publicly available1.

翻译：基于目标的Stense Stance Stream 是寻找目标定位的任务。 Twitter是社交媒体政治讨论的主要来源之一,也是分析 Stance 面向实体的最佳资源之一。 这项工作提出了一个基于目标的Stency 探测新方法, 其方法是利用对最重要且在源推文中争论的目标的答复立场, 检测来源推文本身, 且不局限于作为当前最新方法的通常方法的一组预先确定的目标。 我们的新态度导致产生了一个新的内容, 称为阿拉伯语的ExaASC, 该领域资源较少的语言之一。 最后, 我们利用BERT评估了我们的资料, 并达到了70.69个宏观F分数。 这表明我们的数据和模型可以在一般的目标基准识别系统中工作。 该分册是公开提供的。