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

Introduction: User-generated video portals, such as YouTube, are facing the chal-lenge 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 thumb-nail 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 vide-os 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 in-dustry will surely benefit from the model as it will eliminate false and misleading videos from the platform.

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