Paper Title
Clickbait Identifiction from Youtube titles
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Authors
Ujjwal Goel
Keywords
Abstract
The rapid growth of video-sharing and social media platforms, facilitated by Web 2.0 technologies, has led to a surge in user-generated content. Among these platforms, YouTube stands out with its vast user base. This paper addresses the challenge of distinguishing between clickbait and non-clickbait content in YouTube video titles. Previous research has explored various machine learning algorithms for this purpose, but often lacked comprehensive exploratory data analysis (EDA) and overlooked the application of advanced methods like BERT classification. In this study, we conduct an in-depth EDA, preprocess the data, and employ machine learning techniques, including Multinomial Naive Bayes, Support Vector Machine (SVM), Random Forest, and Bidirectional Encoder Representations from Transformers (BERT). Our objective is not only to accurately identify clickbait titles but also to improve user experience, enhance advertiser trust, and optimize YouTube's recommendation system. Through this research, we provide valuable insights into the detection of clickbait, contributing to the evolving landscape of content moderation and user interaction on social media platforms.
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How To Cite (APA)
Ujjwal Goel (November-2023). Clickbait Identifiction from Youtube titles. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(11), d247-d252. https://ijnrd.org/papers/IJNRD2311322.pdf
Issue
Volume 8 Issue 11, November-2023
Pages : d247-d252
Other Publication Details
Paper Reg. ID: IJNRD_209759
Published Paper Id: IJNRD2311322
Downloads: 000121992
Research Area: Engineering
Country: Dehradun, Uttaranchal, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2311322.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2311322
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This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition
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