Decoding Instagram: A Categorical Approach to User Behavior and Trend Analysis (DICAT)

Abstract

Social media platforms like Instagram have become rich sources of data for understanding user behavior, preferences, and trends. Analyzing Instagram databases requires robust methodologies, especially due to the sheer volume and complexity of the data. In this research paper, we propose a categorical approach for Instagram database analysis, aimed at extracting meaningful insights from diverse categorical data present in Instagram's database. We explore various techniques and tools for data collection, preprocessing, analysis, and visualization, focusing on categorical variables such as hashtags, user interests, and content types. Additionally, we discuss the potential applications of this approach in areas such as marketing, user engagement, and trend prediction. Through a comprehensive study, we demonstrate the effectiveness of our proposed approach in uncovering valuable insights from Instagram's vast database.