# Predicting Rogue Content and Arabic Spammers on Twitter
## 4 Proposed Solution  
Our suggested scheme can be introduced as an independent implementation that depends on machine learning (ML) techniques to detect rogue contents and Arabic spammer tweets. Figure 3 illustrates the proposed system architecture. The general idea that underlies our system is to use Twitter as a public platform to capture users' tweet data by using the Twitter API [20]. Data are collected in a database and then directly forwarded to implement a data pre-processing step that contains several pre-filter functions, which are needed to manage the variable and unpredictable nature of the language used in tweets. Most tweets are extremely likely to contain some type of grammar or spelling errors, emoticons, symbols, pictographs, and flags.  
Figure 3: The architecture of the proposed system.  
After pre-filtering, the system applies our developed lightweight feature engineering to extract useful information. These features are based on tweet text and Twitter user profiles. We adopted attributes that apply to the processing of natural Arabic language. It is worth noting that processing Arabic content is not straightforward since Arabic is a Semitic language. Semitic language properties that are still in their original phases introduce more issues to the processing of natural language compared with other languages [21]. One of these difficulties is that written diagrams are rarely used. Two words look identical without diacritics and are indistinguishable to machines. This decreases the efficiency of syntactic traits based on words [22]. Arabic is a language of inflection and derivation. This creates a serious problem for the removal of Arabic text and thus for the extraction of lexical attributes [23]. Proper nouns do not begin with a capital letter as they do in English, so extracting proper nouns from Arabic text is difficult [23].  
Next, when the engineered features are generated, the system applies a feature selection technique to reduce the number of features, which reflect the performance of the classification algorithm. The main reason for using a feature selection technique rather than any other feature reduction technique is that our random forest classifier has its own methodology to rank features by their importance and select only the features that are important for the classification. Feature selection using random forest is classified as an embedded technique. Embedded techniques combine the capabilities of filter and wrapper methods. Algorithms that have their own built-in feature selection techniques implement them. Some of the advantages of embedded techniques are their extremely high accuracy and their generalizability and interpretability.  
Lastly, the supervised learning data are divided into data sets for training and testing. The training data set is used to train the supervised classification method, which relies on the classification by random forest to form predictions for the study. On the other hand, the test data set is used to evaluate the trained models and determine performance in terms of accuracy, receiver operating characteristic (ROC), sensitivity, and specificity [24].