Abstract
When people use social networks, they often fall prey to a clickbait scam. The scammer attempts to create a striking headline that attracts the majority of users and attaches a link. The user follows the link and can be redirected to a fraudulent resource where the user easily loses personal data. To solve this problem, a Blockchain-enabled deep recurrent neural network (BDRNN) is proposed to detect the nature safe and malicious clickbait from the contents. The proposed BDRNN consists of three phases: analysis of clickbait and source rating, clickbait search process and multi-layered clickbait detection. The analysis of clickbait and source rating phase helps to analyze different sources to detect the clickbait and also rating the content-sources. To achieve the clickbait analysis and source rating, the detection of blocklisted/allowlisted source and source rating check algorithms are introduced. The clickbait search process is accomplished by incorporating the binary search features for a faster and more efficient search process for malicious content-detection. The multi-layered clickbait detection is main phase of the proposed BDRNN that consists of three models: content-to-vector model (layer-1), deep neural network model(layer-2), and Blockchain-enabled malicious content detection model (layer-3). These models collectively detect the malicious and safe clickbait from the contents. The extensive experiments are conducted to determine the effectiveness of the proposed BDRNN model and compared with the existing state-of-the-art neural network models designed for clickbait detection, and the result demonstrates that the proposed BDRNN model outperforms the counterparts from the, accuracy, link detection, memory usage, analogous perspectives, and attacker's successful content capturing rate.