CNN-FastText Multi-Input (CFMI) Neural Networks for Social Media Clickbait Classification

Introduction: User-generated video portals, such as YouTube, are facing the challenge of Clickbait. These are used to lure viewers and gain traffic on specific content. The real content inside the video deviates from its title. and a thumbnail. The consequence of this is poor user experience on the platform. Methods: The existing identification techniques either use pre-trained models or are restricted to text only. Other video metadata is not considered. To tackle this situation of clickbait, we propose a CNN-Fast Text Multi-Input (CFMI) Neural Network. The method employs a self-developed convolutional model, combined with different other video metadata. The thumbnail of any video plays a vital role in gathering user attention; hence, it should also be addressed. With greater expressiveness, it depicts and captures the parallels between the title and thumbnail and the video content. Results: This research also compares the proposed system with the previous works on various parameters. With the usage of the proposed network, the platforms can easily analyze the videos during the uploading stage. The future belongs to Post Quantum Cryptography (PWC), we reviewed various encryption standards in this paper. Conclusion: In Industry 4.0, every data bit is crucial and must be preserved carefully. This industry will surely benefit from the model as it will eliminate false and misleading videos from the platform.