## 3 Materials and Methods
### Data Collection  
We collected 72,321 Arabic news items from Twitter. The dataset can be obtained from github.com ([[https://github.com/[PERSON]/Clickbait-Headlines#clickbait-headlines](https://github.com/[PERSON]/Clickbait-Headlines#clickbait-headlines)]([https://github.com/[PERSON]/Clickbait-Headlines#clickbait-headlines](https://github.com/[PERSON]/Clickbait-Headlines#clickbait-headlines))) (accessed on 1 October 2021). For this purpose, we implemented a special crawler that can access breaking news on social networks by feeding the name of the public breaking news agencies. Often, Twitter APIs return tweets in JSON format. However, because many features are not helpful for the proposed model, the used crawler filters out and saves all the collected information from user profile and shared content in comma-separated values (CSV) format. The details of the collection process through multiple feature analysis are shown in Algorithm 1. In addition, the full description of the features used is presented in Tables 2 and 3. ```
Input: A list of public Twitter breaking news agencies' profiles \(N\) Output: Unlabelled dataset with UFs and CFs For each profile \(p\in N\)do: Access public page of \(p\) Retrieve all shared tweets \(t^{p}\) Pull out using Twitter APIs tweet's features (USs) If \(t^{p}\) contains an external URL Then: Visit the external webpage \(p^{e}\) For all html tags in \(p^{e}\)do: Find html tag that contains news full text (CFs)Compute similarity score between \(t^{p}\) and \(p^{e}\) End End if Store the extracted features in csv format End

Figure 1: The proposed multiple feature approach for detection of clickbait news. ### Data Annotation

Once we obtained the final dataset by using the implemented crawler, we prepared a baseline dataset from the retrieved dataset. Every shared tweet was labelled as a clickbait or legitimate by asking three media professionals to volunteer in judging 12,321 tweets and their associated news. They were asked to access the external webpage by following the URL link provided with tweet and comparing the tweet's body and headline with the full text in the destination webpage. To facilitate this job, we provided them with examples showing what clickbait news looks like. Table 4 shows a guideline for how to classify the content of the shared tweets. \begin{table}
\begin{tabular}{c c c} \hline \hline
**\# Feature** & **Feature Name** & **Description** \\ \hline \(\mathit{UF}^{1}\) & User ID & Every user has one unique ID. \\ \(\mathit{UF}^{2}\) & Name & The name of the user who post news \\  & & on Twitter \\ \(\mathit{UF}^{3}\) & Screen name & The screen name that this user identifies \\  & & themselves with. \\ \(\mathit{UF}^{4}\) & Date of join & The creation time of the user account \\ \(\mathit{UF}^{5}\) & \#Url & Number of URL provided by the user in \\  & & association with their profile \\ \(\mathit{UF}^{6}\) & Profile description & A text that shows how the user describs \\  & & his/her account \\ \(\mathit{UF}^{7}\) & Verified & A boolean indicator shows whether the user \\  & & has a verified account or not \\ \(\mathit{UF}^{8}\) & Count of followers & Total number of followers \\ \(\mathit{UF}^{9}\) & Count of friends & Numeric value indicates how many friends \\  & & that the user has \\ \(\mathit{UF}^{10}\) & Count of favorites’ accounts & Numeric value indicates how many tweets \\  & & this user has liked \\ \(\mathit{UF}^{11}\) & Count of public lists & Total number of public lists that this user is a \\  & & member of. \\ \(\mathit{UF}^{12}\) & Location & The geographical location \\ \(\mathit{UF}^{13}\) & Hashtage & The associated hashtag with the post \\ \(\mathit{UF}^{14}\) & Lang & The post language \\ \(\mathit{UF}^{15}\) & Number of post shared & Total number of content shared by the user \\ \hline \hline \end{tabular}
\end{table} 
Table 2: User-based features.