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

TL;DR

This paper introduces ExaASC, a new Arabic stance detection corpus based on Twitter replies to targets within source tweets, utilizing BERT for evaluation and demonstrating the system's effectiveness.

Contribution

It presents a novel target-based stance detection method for Arabic, creating a new corpus that does not rely on pre-defined targets, and evaluates it with BERT achieving a 70.69 F-score.

Findings

Achieved a 70.69 Macro F-score with BERT on the new corpus.

Proposed a target detection approach based on reply stance rather than pre-defined targets.

Provided a publicly available Arabic stance detection dataset.

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 available1.