## 1 Introduction
The proposed approach achieved average areas of 99.4%, 95%, and 90% under the ROC curve using Word2 vec, TF-IDF, and n-gram count. [PERSON] [7] also used a CNN model to classify a manually constructed news corpus obtained from Reddit, Facebook, and Twitter social networks into clickbait and non-clickbait. As feature selection methods, they used Click-Word2 vec and Click-scratch. The highest results that they achieved were 89% accuracy with 87% ROC-AUC score for Click-scratch features and 90% when the Click-Word2 vec was used. [PERSON] et al. [8] also proposed a hybrid model where a CNN model is combined with LSTM. They found that the CNN-LSTM model when implemented with pre-trained GloVe embedding yields the best results, based on accuracy, recall, precision, and F1-score performance metrics. They also identify eight other types of clickbait headlines: reaction, reasoning, revealing, number, hypothesis/guess, questionable, forward referencing, and shocking/unbelievable. They also found that shocking/unbelievable, hypothesis/guess, and reaction clickbait types to be the most frequently occurring types of clickbait headlines published online. Although several machine learning approaches have been proposed to detect clickbait headlines, most of these recent methods are not very robust. The previous studies used hybrid categorization techniques such as Gradient Boosted Decision Trees, linear regression, Naive Bayes and random forest methods, SVM, decision tree, logistic regression, and convolutional neural network deep learning. Most of these studies used datasets with headlines written in English. However, this paper uses an Arabic language dataset and proposes a comprehensive approach that includes three main phases: data collection, data preparation, and machine learning model training and testing phases. This dataset was pre-processed and then the most important features were selected using ANOVA F-test. Several machine learning methods were then applied which include random forest (RF), stochastic gradient descent (SGD), Support Vector Machine (SVM), logistic regression (LR), multinomial Naive Bayes (NB), and k-nearest neighbor (k-NN). Hyper-parameter tuning methods were applied to ensure finding the optimal settings. Finally, the ML models were evaluated and the overall performance is reported here. The key contributions of this paper are as follows:  
* We constructed the first Arabic clickbait headline news dataset. The raw dataset is available publicly for research purpose. * We extracted a set of user-based features and content-based features for the constructed Arabic clickbait dataset. * We implemented six machine learning-based classifiers, including Random Forest (RF), Stochastic Gradient Descent (SGD), Support Vector Machine (SVM), Logistic Regression (LR), Multinomial Naive Bayes (NB), and k-Nearest Neighbor (k-NN). * We proposed an effective approach for enhancing the detection process using a feature selection technique, namely a one-way ANOVA F-test. * We conducted extensive experiments, and the results show that the proposed model enhances the performance of some classifiers in terms of accuracy, precision, and recall.