• Fardeen Kachawa
• Sujal Bhatt
• Bhavesh Choudhary
• Kaif Siddique
• Neelam Phadnis

In the age of information proliferation on the internet, the ubiquitous presence of clickbait content poses a serious challenge to content consumers, affecting digital literacy and cybersecurity. This project introduces the development of a Clickbait Detection System, leveraging advanced deep learning techniques to empower users with the means to discern and categorize clickbait effectively. This system is driven by the urgent need to counter the dissemination of deceptive and sensationalized content, providing users with a tool to make informed decisions about the content they engage with. The core objectives of the project are twofold: First, to create a user-friendly web-based application using the Python programming language and the Flask framework, allowing users to easily upload images and receive real-time clickbait detection results. Second, to implement advanced deep learning algorithms that process and analyze images for clickbait elements, utilizing Optical Character Recognition (OCR) for text extraction and Convolutional Neural Networks (CNNs) for image recognition. These deep learning models, enhanced through extensive training, are poised to provide accurate clickbait detection results. In conclusion, this initiative offers a thorough and user-centered solution to the urgent problem of clickbait material. Modern deep learning algorithms are incorporated into a user-friendly web application to give people the tools they need to safely traverse the digital world while promoting digital literacy and boosting online security.

Clickbait, Deep Learning, Machine Learning, CNN, Optical Character Recognition(OCR)

"BAITAWARE : A CLICKBAIT DETECTION SYSTEM USING DEEP LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.i603-i610, April-2024, Available :http://www.jetir.org/papers/JETIR2404880.pdf

"BAITAWARE : A CLICKBAIT DETECTION SYSTEM USING DEEP LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppi603-i610, April-2024, Available at : http://www.jetir.org/papers/JETIR2404880.pdf