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@article{174224,
        author = {Sandaka S V Swamy Charan and Gavara Naveen and V Vidya Sagar and Naimisha Ganivada and Silaparasetty pranay},
        title = {Clickbait Prevention: AI-Enhanced Thumbnail Screening},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {3413-3418},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174224},
        abstract = {Clickbait thumbnails are increasingly used on video platforms, such as YouTube, to attract clicks by misleading viewers with exaggerated or irrelevant visual content. This not only affects user experience but also spreads misinformation. This paper presents an AI-driven solution for clickbait thumbnail detection by analyzing the correlation between text on the thumbnail and the corresponding video transcript. Our approach integrates several cutting-edge AI techniques, including Convolutional Neural Networks (CNN) for image processing, Optical Character Recognition (OCR) for text extraction, and BERT (Bidirectional Encoder Representations from Transformers) for semantic similarity analysis. Experimental results show that our system successfully identifies clickbait thumbnails with an accuracy of 89%, demonstrating its potential in enhancing content moderation systems on platforms like YouTube. This research underscores the importance of using AI to promote content integrity and improve digital media quality.},
        keywords = {Clickbait Detection, Convolutional Neural Networks (CNN), Optical Character Recognition (OCR), BERT, Natural Language Processing (NLP), Content Moderation, AI-driven Systems.},
        month = {March},
        }