ExaASC: A General Target-Based Stance Detection Corpus in Arabic Language

Abstract

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 available 1 .

Contextual stance classification using prompt engineering

“…After the initial SemEval and RumourEval shared tasks, multiple stance classification datasets and models have been publicly released. These include, for instance, studies devoted to Arabic [Alhindi et al 2021, Jaziriyan et al 2021, Portuguese [Won and Fernandes 2022], German [Gohring et al 2021], and multilingual scenarios [Chen et al 2022]. Moreover, although most studies are purely text-based, the issue of multimodal stance classification (e.g., combining text and social media relations or other knowledge sources) has also been investigated [Sakketou et al 2022].…”

Detecting Stance of Authorities towards Rumors in Arabic Tweets: A Preliminary Study

“…A notable exception is the work done by Haouari et al [19] that utilized the replies, their structure, and repliers' profile features to verify Arabic COVID-19 rumors. Several studies addressed Arabic stance detection in Twitter; however, the target was a specific topic not rumors [15,22,6]. A few datasets for stance detection for Arabic claim verification were released recently, where the evidence is either news articles [10,3] or manually-crafted sentences [23].…”