# Predicting Rogue Content and Arabic Spammers on Twitter
## 1 Introduction  
Twitter is a platform that allows users to compose messages of 140 characters or less. These messages are known as tweets and can include text, short videos, images, and hyperlinks. Twitter usernames start with the prefix @. Users of Twitter build their social networks by interacting with fans and followers. Tweets generated by users appear on their homepage and the timelines of their followers, and they can be discovered by Twitter's search engine. The tweets are often spread by followers who click the \"Retweet\" icon. The tweet is often relayed continuously, as is the username prefixed by @, which is included in the tweet. The subjects of tweets are frequently indexed by hashtags related to each subject. All Twitter hashtags are preceded by the hash (#) symbol and can even be found by using Twitter's search engine [1]. Twitter is a popular online social network (OSN) in Middle Eastern countries, especially Gulf countries such as Saudi Arabia, which ranks second for the number of Twitter users worldwide. In our previous investigation, we found that about three quarters of the tweets with trending hashtags in Saudi Arabia were spam messages. This estimation is backed by independent reports that place Saudi Arabia as the second most common \"global target for online spam and other forms of cyber-violation\" and as the \"most spammed country in the world for three years in a row with an 83.3% spam rate\" [2]. A deeper analysis showed an even higher percentage of automatically generated tweets. Therefore, in addition to Twitter's resources being consumed by malicious accounts rather than intended users, any statistics or opinions mined using these trends are unreliable. In particular, it is logical to questionany reports that indicate Saudi Arabia as the Arab nation with the highest number of active Twitter users [3] or that show \"booming usage\" with a penetration higher than 51% [4]. Much research has been conducted on data mining and machine learning for the English corpus [5; 6; 7], while little research has been carried out on Arabic text. This is mainly because of its morphological complexity and the limited availability of compatible Arabic language software. In addition, Arabic words have different meanings and spellings, the sentence structure of Arabic differs from that of English, Arabic letters have different shapes depending on the letter location in a word, and Arabic words are either masculine or feminine and come in three different formats: singular, dual, or plural [8]. Arabic nations are facing challenges in the detection of rogue accounts and spam in Arabic tweets. The use of keyword lists to identify spam accounts approach used by current censorship systems implemented in Arab nations [9]. In this paper, we focus on classifying rogue and spam content in Arabic tweets using machine learning algorithms. We explain the data collection approach and the features engineered. Then, we present the evaluation of our proposed Arabic approach with a random forest classifier, and we report the minimum number of features that can produce the best results. Much research has been conducted on the detection of spammers in the English language, and comparatively little attention has been paid to determining the minimum number of features that give approximately the same result. We summarize the contributions of this work in the following points:  
* We obtained one of the largest data sets for rogue and spam accounts with Arabic tweets by directly collecting the tweets from such accounts. * We surveyed more than 180 Arabian participants by administering Twitter security-related questionnaires to measure and discover the area of security that could be of the most concern to Twitter users. * We enhanced and engineered 47 of the most effective and simple features based on tweets, profile content, and social graphs. * Using the random forest feature selection method, we evaluated and compared different numbers of features and found that the method with 16 features performed best.