## 3 Materials and Methods
### Data Collection
\begin{table}
\begin{tabular}{c c c} \hline \hline
**\# Feature** & **Feature Name** & **Description** \\ \hline \(\mathit{CF}^{1}\) & \#Url & Number of external URLs provided by the user in association with news \\ \(\mathit{CF}^{2}\) & Source & Name of the source of the news article. \\ \(\mathit{CF}^{3}\) & Headline & The headline of the news article for catching \\ \(\mathit{CF}^{4}\) & Tweet Text & The headline of the reader’s attention \\ \(\mathit{CF}^{5}\) & Body Text & The full news in readable format, often with \\  & & external content \\ \(\mathit{CF}^{6}\) & Retweet count & Total number of times this tweet has \\  & & been retweeted. \\ \(\mathit{CF}^{7}\) & media & Boolean value indicates whether there are \\  & & associated images or videos \\ \(\mathit{CF}^{8}\) & Similarity score & The score for similarity between headline \\  & & text and body text. \\ \(\mathit{CF}^{9}\) & Creation date & The posted date of the news content \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Content-based features. As shown in Table 4, there are seven categories that the volunteers could use to label each post as clickbait news. In case of unclearness or doubt about which class the post belongs to, the post is labelled as \"incomplete\". Every content text in the baseline dataset has three labels, one provided by each annotator. To assign the final class label, we applied the majority voting algorithm and labelled the content as clickbait or legitimate news. Table 5 shows the details of the baseline dataset, which includes 4325 items of clickbait news and 6743 legitimate items. The news items that are labelled as incomplete were later removed from the dataset. The remaining baseline dataset contained 11,068. As the size of our final baseline dataset was quite small (17% of the original dataset), we decided to apply a pseudo-labelling learning technique to enhance the performance of the ML model. PLL is an efficient semi-supervised technique that can be applied to utilize unlabeled data while training ML models. As shown in Figure 1, the ML model is trained first on the labeled data (in this case: the baseline dataset). The model then predicts the labels of unlabeled data. The predicted pseudo-labels are assigned as target classes for unlabeled data and combined with the original baseline dataset (labeled data). Finally, the produced new dataset is then used to train the proposed ML-models. After applying PLL

\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Definition** & **Example of Arabic Clickbait** & **Translation of the Arabic** \\  & & **News** & **Clickbait News** \\ \hline \multirow{4}{*}{Ambiguous} & Title unclear or & \multirow{4}{*}{This matter did not happen in the kingdom.} \\  & confusing to spur & & kingdom. \\  & & & This man is kind father. In \\ \multirow{4}{*}{Exaggeration} & The title exaggerates & \multirow{4}{*}{This man is kind father. In} \\  & the content of the & & America, the customer eats for \$20 \\  & landing page. & & and leaves 1400 tips \\ \multirow{4}{*}{Inflamm-story of inappropriate/vulgar words.} & \multirow{4}{*}{Doctors under the name of} \\  & & & medicine “criminal doctors” \\ \multirow{4}{*}{Teasing} & \multirow{4}{*}{Omission of details} \\  & from title to build & \multirow{4}{*}{Overnight... an American wins a} \\  & & & billion dollars \\ \multirow{4}{*}{Format-ing} & \multirow{4}{*}{Excessive use of} & \multirow{4}{*}{“How are you blind and a} \\  & punctuation or & & photograp”?”!.. This negative \\  & exclamation marks. & & phrase was the launch of “Al- \\ \multirow{4}{*}{Wrong} & \multirow{4}{*}{Just plain incorrect} & \multirow{4}{*}{10 things rich people do that you} \\  & article: factually wrong.