# Predicting Rogue Content and Arabic Spammers on Twitter
## 3 Literature Review  
This section reviews the most significant studies on spam detection and machine learning related to users of Twitter. One of the most famous hallmarks of the digital age is the popularization of social media sites. Individuals use these platforms to communicate with family, friends, and even business associates. Digital social platforms suffer from spamming because users with unethical intent use them for self-serving reasons. Commonly targeted sites include \"Twitter, Facebook, and LinkedIn\" [12]. The main types of spam include the duplication of information, continuous reposts of topics for marketing purposes, and distribution of unwanted messages. The present literature review discusses how machine learning can be used to recognize spam from Twitter accounts that are predominantly in Arabic languages. All the algorithms and associated approaches strive to detect whether the posted text has positive or negative sentiments, together with the extent of profanity [13]. There are other reasons to examine social media posts, especially in the modern age, in which everything is linked to cyberspace. The aim of this review is to focus on Arabic Twitter spam and the use of machine learning as a possible solution. Twitter is among the most frequently used sites, and it continues to grow with each passing year. Its large number of users also implies a high percentage of spam messages [6]. Twitter allows short messages with only text and HTTP links available to users. Individuals who spread spam content rely on their anonymity to communicate with legitimate users. It has become crucial for developers to establish ways to prevent spam content. Among the many forms of doing so is the use of traditional classifiers for detecting spam; these include random forest (RF), naive Bayesian (NB), support vector machine (SVM), and \(k\)-nearest neighbor (\(K\)-NN) schemes [6]. The two researchers found that random forest was the best method for this purpose, with a precision of 95.7% on a small Twitter data set. The process is driven by the growing need for spam detection by information filtering. Under the Twitter umbrella, researchers have noted that the limited length of posts does not provide the required number of word occurrences. Thus, traditional methods, such as \"bag-of-words\", are not ideal [14]. This discovery marked the beginning of more sophisticated techniques. On a structural level, spam detection entails checking for certain words in a tweet. The same concept applies regardless of whether a person decides to use simple or complex machine learning systems. [PERSON] et al. [15] provided a simple means of understanding the concept. People may choose to filter information for various reasons, such as the need to categorize information, remove pornographic content from the media stream, or prevent children from seeing specific posted messages. All these objectives lead to machine learning interactions with the Twitter API and other interfaces. A more in-depth analysis of spamming problems reveals engineering algorithms such as NB IBK (which is may refer to Ibk algorithm, implements the \(k\)-NN algorithm) as means of finding solutions to the problem. As observed by [PERSON] and [PERSON] [6], a person can pick the best algorithm to use though empirical examination. Interestingly, [PERSON] and [PERSON] [12] carried out a similar exercise and found that random forest had the highest success at 92.95%. A researcher must experiment to determine the best algorithm to use before proceeding with further analysis. There is no particular algorithm that surpasses all others under all circumstances; this explains the need to experiment with various approaches. Before moving to advanced classifier techniques, it is essential to understand the reason that most researchers have dismissed SVM classifiers such as bag-of-words and bag-of-means. [PERSON] et al. [16] used hashtags and N-grams to screen out adult Arabic content.