InstaFlow Insights: Mapping Influence Networks

Social media platforms have transformed digital marketing by enabling influencers to shape audience engagement and brand visibility. This work presents InstaFlow Insights, an analytical framework designed to study influencer performance and engagement behavior on Instagram. The system analyzes influencer data using features such as follower count, posting frequency, average likes, engagement rate, and hashtag usage. Data preprocessing techniques are applied to clean and structure the dataset before analysis. A machine learning–based regression model is used to predict engagement trends and identify factors that contribute to influencer success. In addition to predictive analysis, statistical evaluation methods are used to interpret engagement patterns and compare influencer performance metrics. Experimental results demonstrate that engagement prediction can be effectively modeled using structured influencer features, providing meaningful insights into content reach and audience interaction. The proposed framework helps marketers and researchers understand how posting behavior and content strategies influence engagement outcomes. The system offers a data-driven approach for influencer evaluation and supports decision-making in digital marketing strategies. Plain Language Summary: This research introduces InstaFlow Insights, a tool that helps people understand what makes an Instagram influencer successful. By looking at things like how many followers someone has, how often they post, and the types of hashtags they use, the system can predict how much “engagement” (likes and comments) a post might get.