Predicting Virality Before Uploading Videos in Youtube

Authors:

G.Bhagya Lakshmi, K.Jatin , N.Bhargav , K.Lohitha , S.Surya

Abstract— In today’s digital era, video content plays a crucial role in communication, entertainment, and marketing, especially on platforms like YouTube, where predicting a video’s success before uploading remains a significant challenge. This paper presents an AI-based system designed to estimate the viral potential of videos prior to publication, enabling creators to make informed decisions and improve content quality. The system works by extracting audio from the video and converting it into text using a speech recognition model, after which the generated transcript is processed using a language model to create captions, hashtags, and SEO-friendly descriptions. In addition to content generation, the system analyzes important features such as video duration, title length, transcript consistency, and sentiment score, which are known to influence audience engagement. These features are then used as input for machine learning models, including ensemble techniques, to predict the expected reach and virality score of the video. Furthermore, the system provides actionable insights such as the best time to upload, audience targeting suggestions, and content improvement recommendations. By combining speech processing, natural language processing, and predictive modeling, the proposed approach reduces reliance on guesswork and helps creators optimize their content before publishing. The results demonstrate that this system can effectively support data-driven content creation and enhance overall engagement and visibility on video-sharing platforms.

Index Terms—Content Optimization, Machine Learning, Natural Language Processing, Video Virality Prediction.