# Enhancing Detection of Arabic Social Spam Using Data Augmentation and Machine Learning
## 1 Introduction  
Social networks such as Facebook, Twitter, and Amazon have become important platforms for satisfying people's needs for social interactions, information acquisition, and dissemination. However, such unparalleled convenience supports the operations of malicious entities, such as virus distribution, fraud, falsifying product reviews, and link farming. In social networks, spam is defined as unwanted, malicious, or inappropriate content or behavior that can take many forms, such as microblogs, malicious links, messages, fake friends, fake reviews, etc. Spammers can earn money directly (e.g., by writing fake reviews to advertise their products) or achieve popularity via spanning (e.g., by building connections with many other users) [1]. Spam decreases the communication quality provided by social media platforms. It pollutes social networks and distorts people's perceptions of internet information. The user experience will drastically decrease if someone is exposed to highly undesirable information, which would result in customer losses for the social service provider [1]. Therefore, social platforms must develop algorithms to detect spam content. According to [PERSON], [PERSON], et al. [1], the spam protection field in social networks has been approached from two perspectives: The detection of spam and the detection of spammers. Research on spam detection approaches can be classified into user-based spam detection methods, content-based spam detection methods, trend topic methods, graph-based spam detection methods, and hybrid spam detection methods [1]. To protect legitimate users from being affected, spam detection aims to develop effective and efficient methods for automatically identifying spam content and its originators. Social spam refersto unwanted or malicious content posted on the social network platform. It includes scam malware, phishing website, commercial advertisement, and promotion information, disguised posts that appear legitimate but contain malicious URLs, reply spams, scam software, information about promotions, and reposting spam that attaches junk information behind original posts [2; 3]. Due to the subjective nature of spam detection and the domain used, which is dependent on the researcher's definition of spam, finding a consistent definition is challenging. In this paper, we focus more on detecting the social content spam category on Twitter. Twitter is one of the most popular online social platforms. Due to its widely used, online spammers use it as a target to spread illegal content, fake news, and online advertisements [4]. Spam detection on a noisy site such as Twitter remains challenging due to the short text and the wide range of language used on social networks [5]. Machine learning is widely used in different fields for automatic learning and detection, including spam detection on Twitter [6]. Previous research has extracted and utilized a wide range of features, from simple to complex, as well as a wide range of learning and classification algorithms, from traditional machine learning techniques to deep learning [6; 7]. There are not enough data to train a high-quality classifier in many machine learning scenarios. Data augmentation can be utilized to resolve this problem [8]. In the spam detection field, the problem of insufficient data is often encountered due to the difficulty and the weak ability of data collection and labeling. The model will be overfitted if the quantity of samples obtained is insufficient to meet the needs of model training. We can overcome the problem of having a limited number of training instances using data augmentation [9]. Furthermore, a common issue in the spam classification task is data imbalance, resulting in poor classification results. If data augmentation is performed, imbalanced data can be transformed into balanced data, or sample imbalance can be minimized, and model performance can be improved [10]. The model's performance is highly dependent on the data's quality.