# Abusive Content Detection in Arabic Tweets Using Multi-Task Learning and Transformer-Based Models
###### Abstract  
Different social media platforms have become increasingly popular in the Arab world in recent years. The increasing use of social media, however, has also led to the emergence of a new challenge in the form of abusive content, including hate speech, offensive language, and abusive language. Existing research work focuses on automatic abusive content detection as a binary classification problem. In addition, the existing research work on the automatic detection task surrounding abusive Arabic content fails to tackle the dialect-specific phenomenon. Consequently, this has led to two important issues in the automatic abusive Arabic content detection task. In this study, we used a multi-aspect annotation schema to tackle the automatic abusive content detection problem in Arabic countries, based on the multi-class classification task and the dialectal Arabic (DA)-specific phenomenon. More precisely, the multi-aspect annotation schema includes five attributes: directness, hostility, target, group, and annotator. We specifically developed a framework to automatically detecting abusive content on Twitter using natural language processing (NLP) techniques. The developed framework used different models of machine learning (ML), deep learning (DL), and pretrained Arabic language models (LMs) using the multi-aspect annotation dataset. In addition, to investigate the impact of the other approaches, such as multi-task learning (MTL), we developed four MTL models built on top of a pretrained DA language model (called MARBERT) and trained on the multi-aspect annotation dataset. Our MTL models and pretrained Arabic LMs enhanced the performance compared to the existing DL model mentioned in the literature. abusive content; dialectal Arabic (DA); NLP; DL; multitask learning +
Footnote †: journal: Medical  
due to the paucity of high-quality annotated datasets. However, binary judgments of hate speech (e.g., hate or not hate) are known to be unreliable [5; 6; 7]. Furthermore, recent studies have recommended the use of multiclass detection tasks and a multilabel annotated dataset [5; 6; 7; 8] to enhance the detection task beyond the binary classification. The second issue in the automatic abusive Arabic content detection task is that it fails to tackle the dialect-specific phenomenon. This is because the Arabic tweets are written in dialects, and it requires such research and solutions to detect abusive language in tweets. Thus, this paper aims to tackle the problem using a fine-grained classification task in Arabic. Thus, we searched the available dataset that goes beyond the binary classification task and used it for automatic abusive content detection in Arabic. Generally, we found seven datasets (see Section 3.1) and discovered that the annotation of the available datasets has different schema, e.g., binary, ternary, multiclass, and multi-aspect abusive content. The only available dataset that used a fine-grained and multi-aspect annotation procedure was the muti-aspect hate speech dataset which was constructed by [PERSON] et al. [9]. The multi-aspect hate speech dataset includes five attributes (directness, hostility, target, group, and annotator). These attributes, labeled as hierarchical annotation procedures, are illustrated in Figure 1, with each attribute found within the indication labels. Each tweet was assigned into one of five aspects, with each one including a multi-class classification task, except the directness aspect which used a binary task. This dataset was designed for multilingual study purposes; however, we selected the Arabic dataset in our study. We believe that this hierarchal annotation process can detect, categorize, and identified abusive content and targeted groups with the fine-grained detection procedure. In this study, we developed a framework to automatically detect abusive content on Twitter using natural language processing (NLP) techniques.