# Clickbait Detection Using Deep Recurrent Neural Network
## 3 Related Work
However, the proposed method only considers media and news content, and the detection time of harmful/benign instances of clickbait is long. [PERSON] et al. [24] proposed an approach based on recurrent neural network-bidirectional long short-memory to specify words that affect the scores of clickbait ads. Additionally, similarity information between the source and target is specified by utilizing a Siamese net. Subsequently, the convolutional neural network is used to recognize image embedding from a large amount of data, which adds complexity overhead to the introduced solution. The authors conducted experiments on 19,538 social media posts to evaluate their method. Although the proposed method achieved satisfactory accuracy, this solution is not designed for malicious clickbait detection and is limited to attribute similarity scoring and headline classification. [PERSON] et al. [25] proposed a method that balances data evaluation and classification of fake news forecasting. Natural language processing is employed to describe the text and title of the news. To address this issue, classical ensemble methods (CEMs) have been introduced. All of the previously proposed approaches concentrate on performance improvement in terms of accuracy. However, they overlook the importance of detection time and memory usage. Thus, our approach considers these neglected factors (i.e., fast detection time and low memory usage) as well as accuracy. Table 1 compares and contrasts the existing methods. Additionally, Table 2 provides a detailed comparison of differences (low, medium, high, yes, no). In the comparison, the low was <95, medium was >95 && <98, and high was >98. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Clickbait Detection Protocol** & **Features/Characteristics** & **Deficiencies** \\ \hline
[17] & A protocol that uses a firewall to block malicious advertising & The ability to restrict malicious advertisements and prevent clients from revealing sensitive information & Incapable of discovering the filed advertisement \\ \hline
[18] & Blacklist and whitelist detection technique & Blocking suspicious Internet resources and the given information about Internet resources is delicate & Lack of completeness (i.e., it is not capable of providing a complete list of Internet resources). Additionally, it is not superior in terms of accuracy \\ \hline
[19] & Constructing a set of rules to protect clients from malicious resources & The capacity to prevent unverified sources by checking suspicious title content and disabling execution of scripts & Following the suggested rules does not prevent most Internet fraud \\ \hline
[20] & An approach based on artificial intelligence & The ability to detect various cybersecurity attacks using machine learning and predefined signatures & Ineffective in detecting new attacks \\ \hline
[21] & A rule-based method to detect/prevent clickbait and phishing & Some of the provided filtering rules are able to prevent phishing and clickbait & Not all phishing activities are covered by the suggested rules \\ \hline
[22] & A browser-based extension method to detect clickbait & The ability to find and block active scripts that can communicate with clients to reveal personal information & Useful advertisements are also blocked \\ \hline
[23] & Clickbait detection based on deep learning & High performance in terms of accuracy to detect more clickbait on social media. Moreover, it is able to detect headlines that consist of suspicious clickbait on several social media platforms & It is computationally complex because it combines two deep learning architectures (i.e., LSTM and CNN) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the contributions of existing approaches.