## 3 Materials and Methods
### Data Collection
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**ML Classifier**} & \multicolumn{4}{c}{**Experiment**} \\ \cline{2-5}  & **Baseline** & **F\_Values\_5\%** & **F\_Values\_10\%** & **F\_Values\_15\%** \\ \hline RF & 77.97 & 86.83 & 90.21 & 83.67 \\ SGD & 74.72 & 85.72 & 84.59 & 79.19 \\ SVM & 75.89 & 89.31 & **91.83** & 90.37 \\ LR & 77.65 & 75.43 & 75.76 & 75.09 \\ NB & 74.65 & 87.35 & 90.24 & 89.46 \\ k-NN & 76.99 & 73.77 & 65.08 & 65.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Accuracy of different experiments with content-based features only. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**ML Classifier**} & \multicolumn{4}{c}{**Experiment**} \\ \cline{2-5}  & **Baseline** & **F\_Values\_5\%** & **F\_Values\_10\%** & **F\_Values\_15\%** \\ \hline RF & 61.24 & 61.73 & **63.93** & 62.34 \\ SGD & 59.04 & 52.65 & 51.86 & 57.62 \\ SVM & 64.40 & 62.76 & **66.54** & 61.08 \\ LR & 61.87 & 61.87 & 61.87 & 61.87 \\ NB & 61.75 & 62.03 & 60.12 & 61.49 \\ k-NN & 60.57 & 46.83 & 42.72 & 41.33 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Accuracy of different experiments with user-based features only. * Combining user-based features and content-based features shows an improvement in the performance of ML classifiers and only LR and k-NN classifiers did not show any improvement. * The SVM outperforms all other classifiers and benefited more when the proposed feature selection method was used for the combination of user-based features and content-based features. The highest accuracy achieved was 92.16%. * As the total number of features for the combination of user-based and content-based features is 10,251, selecting the top 10% of these features (2194) was more suitable for SVM, which performed well with low dimensionality data. * As shown in the results, using the user-based features achieved lower performance than using the content-based features for all ML methods. Therefore, the proposed model relies more on the content-based features and the combined ones. ## 6 Conclusions

This paper has proposed a comprehensive approach that includes three main phases: data collection, data preparation, and machine learning modeling phases. After collecting the dataset, which is considered the first Arabic clickbait headline news dataset, the pre-processing tasks were performed, which included text cleansing, normalization, stemming, stop words removal, and tokenization. The features of the processed text (content-based features) were then combined with the user-based features and the feature selection was then applied using one-way ANOVA test. Finally, the ML models were applied, which included 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. The experimental results showed a great enhancement when the CFs were used and also when a combination of UFs and CFs was used. The accuracy achieved reached 92.12% using 10% of the top-scoring features, which is better than that reported in many previous studies (discussed in the related works). This enhancement is particularly interesting, as we are dealing with Arabic contents. Future work will investigate the application of several deep learning methods on this Arabic dataset in order to enhance the detection performance. Moreover, collecting more Arabic content to add to the dataset will be a beneficial addition to conducting the analysis. Conceptualization, M.A.-S., F.S., T.A.-H.; methodology, M.A.-S., F.S. ; software, M.A.-S.; validation, T.A.-H., A.A.; formal analysis, F.S., M.H. ; investigation, M.T.A., T.S.A. ; resources, Z.G.A.-M., B.A.M., M.H., A.A., T.S.A. ; data curation, B.A.M. ; writing--original draft preparation, M.A.-S., F.S. ; writing--review and editing, M.A.-S., F.S. ; visualization, F.S. ; supervision, M.A.-S., F.S. ; project administration, M.A.-S., Z.G.A.-M.; funding acquisition, Z.G.A.-M., B.A.M., M.T.A.