SOCIAL MEDIA POST ENGAGEMENT TRACKER AND PREDICTION

Abstract

Social media platforms generate large amounts of data through posts, likes, comments, shares, views, hashtags, followers, and user interactions. Analyzing this data can help understand how audiences respond to different types of content. The Social Media Post Engagement Tracker and Prediction system is designed to analyze these interactions and provide meaningful insights into post performance. The proposed system processes social media post information such as post type, caption, hashtags, posting date, posting time, likes, comments, shares, views, reach, followers, and engagement rate. Data preprocessing techniques are applied to clean the collected dataset by handling missing values, duplicate records, inconsistent formats, and invalid entries. The processed data is then prepared for analytical and predictive tasks. The system calculates important performance metrics such as total likes, comments, shares, views, reach, engagement rate, average engagement, and post-performance trends. An interactive dashboard can present these metrics through KPI cards, charts, graphs, tables, and filters. Users can analyze engagement based on post type, date, time, hashtag, campaign, or other available attributes. The prediction module uses historical social media data and Machine Learning techniques to estimate the expected engagement of future posts. Factors such as posting time, content type, historical engagement, follower count, hashtags, and reach can be used as predictive features. The system can also classify posts into different engagement levels such as low, medium, or high. Overall, the proposed system combines social media analytics, performance tracking, visualization, and predictive modeling in a unified platform. It can help content creators, marketers, businesses, and analysts understand audience engagement patterns. Future enhancements can include real-time social media integration, sentiment analysis, hashtag recommendations, optimal posting-time prediction, and automated content-performance alerts.