International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Initiating and involving oneself in social media has become involved in all people's lives today. It allows people to connect with people in different places and share content, information, experience, ideas, and more. Since so many people take part in these activities every day, businesses and organizations are getting into the market as well by marketing, advertising, promoting their brands, and getting more clients. It is also possible to analyse user activity with post engagement. This work used datasets of different features from the past to understand engagement of post and applied Machine Learning (ML) methods to analyse and interpret user activity and measure the amount of engagement of users. The datasets included caption and post time, media type, post length, CTR (click-through rate), ad-interaction time, and hashtag. The datasets also used Machine Learning (ML) algorithms to predict how many interactions a post may get. The results are displayed in graphs that show cluster of users and the engagement activity of each post.

Keywords: Machine Learning, Social Media, Engagement Metrics, Predictive Analytics.

Downloads: | DOI: 10.17148/IJARCCE.2025.14816