Big Data-Driven Analysis of User Behavior and Trends on Social Media Platforms

In the era of digital communication, social media platforms have become rich sources of user-generated data, offering deep insights into individual preferences, behaviors, and emerging societal trends. This research focuses on leveraging Big Data technologies and Machine Learning (ML) techniques to analyze user behavior and detect evolving patterns across various social media platforms. By collecting and processing vast amounts of structured and unstructured data — including posts, likes, shares, hashtags, and comments — this study implements scalable data pipelines and predictive algorithms to uncover hidden trends and correlations.The framework integrates tools such as Hadoop, Spark, and NoSQL databases for data handling, along with ML models like clustering, sentiment analysis, and classification to interpret user activity. Through case studies and empirical evaluation, the proposed system demonstrates how real-time trend analysis and user profiling can support applications ranging from targeted marketing to public opinion monitoring and crisis detection.The findings highlight the potential of Big Data and ML as a powerful combination for deriving actionable insights from the noisy and dynamic social media environment. This study contributes to the fields of social media analytics and intelligent systems by offering a scalable approach to understanding digital user behavior at scale