# Predicting Rogue Content and Arabic Spammers on Twitter
## 3 Literature Review
Additionally, research by [PERSON] and [PERSON] [18] compared DT, NB, and rule-based W-JRIP (which is a type of propositional rule learning and repeated incremental pruning to produce error reduction (RIPPER) proposed by [PERSON]), and they chose DT as the most suitable classifier. Their preferred choice achieved an accuracy of 85.6% when coupled with light stemming tactics. Their findings proved that different research approaches yield different results; however, advanced machine learning classifiers provided better accuracy than the simple classifiers. This study showed that the proposed techniques have functional versatility. They used these classifier methods to check the popularity of news articles on Arabic Twitter. For example, apart from the statistical figures found, they realized that users reviewed the website and the source of the material to rate their interest in the article. Arabic users checked the article source more than they checked the title and description of the content. This implies that they value honest news more than the vocabulary used. Another study that focused on Arabic spam accounts helped decipher the reason that some of these accounts go undetected. [PERSON] and [PERSON] [2] provided a realistic view of spamming in Saudi Arabia and some tactics used by spammers to avoid detection. Consequently, to find these abusive accounts, they developed an approach to handling these methods of evasion. These two authors found that Saudi Arabia had about 75% of the spam content found worldwide [2]. It has become imperative to find ways to reduce this figure through customized maneuvering. Furthermore, the country suffers from economic and political challenges as a result of harmful spam in social media. Evasion techniques include \"using semantic synonyms to texts, buying followers, the use of clusters formed by spammers, and mimicking the behavior of authentic accounts\" [2]. These strategies lower the accuracy of currently used machine learning techniques. This discovery means that the next generation of classifiers needs to include these evasion techniques as part of the accuracy measurement. The above literature review uses numerous credible sources to explain the concept of spam detection and its use in combating profanity in Arabic Twitter. There have been efforts to include other purposes of information filtering, such as marketing purposes and child protection. However, the main goal has remained to establish a machine learning approach that can detect certain words and trends. The literature review mainly focuses on the Arabic language because of the limited number of studies carried out to monitor Arabic Twitter. Saudi Arabia generates three quarters of the Twitter spam found in the world. The analysis also reveals that advanced machine learning algorithms, such as RF, DT, and NB, are more effective compared with simple algorithms such as bag-of-words. Lastly, the Arabic language provides a challenge for web developers because of its complexity in spelling, symbolism, and the existence of different dialects across the world. As a comparison between the related work approaches that attempted to detect rogue contents and Arabic spammers on Twitter with our proposed approach based on the results and novelty keys. To the best of our knowledge, we found about three similar related studies to ours. The state of the artwork by [PERSON] et al. [1] gathered 1,300,000 tweets and they extracted more than 1400 number of features, where they found that 100 features have a better performance than a larger number of features. They compared three classification models: NB, SVM, and DT, where the NB classifier had the best performance with 10 tweets and 100 features, which achieved with 90% accuracy rate. Another important work was by [PERSON] and [PERSON] [2] where they used a large collected data set of over than 23 million Arabic tweets, and a manually labeled sample of over than 5000 tweets. They randomly chose 10% of the 55,239 obtained tweets for classification and validation.