Understanding Social Media Behavior in the USA: AI-Driven Insights for Predicting Digital Trends and User Engagement - Hasanuzzaman, Muhammad; Hossain, Miraz; Rahman, Md Majedur; Rabbi, Md Masud Karim; Khan, Medhat Mohiuddin; Zeeshan, MD Abdul Fahim; Sarker, Babul; Rabbi, Md Nazmul Shakir; Kawsar, Mohammed (Journal of Ecohumanism, 2025)
The swift advancement in social networking platforms has radically shifted the patterns and nature of how people connect with services and brands in America. The utmost objective of this research project was to implement artificial intelligence together with machine learning approaches for creating predictive models that forecast digital pattern development as well as user commitment through social media interactions within the United States. The data used in this analysis are posts aggregated from two leading online platforms, X-Twitter and Reddit, and consist of user-generated material covering a multifaceted set of topics and opinions. X-Twitter posts are current and real-time and give insight into what is currently being discussed and the opinions that are making headlines, whereas Reddit content provides extensive commentary and user engagement on numerous subreddits. To make the dataset more comprehensive and richer in information, extensive engagement metrics like likes, shares, and comments are used to extend its reach and provide insight into the extent to which users engage with the material presented to them. For this research, we used the multi-model approach so that there would be an exhaustive study and strong predictions, by implementing models such as Logistic Regression, Random Forest, and XGB-Classifier. To assess the models properly, we made use of several performance metrics like Accuracy, Precision, Recall, and F1-score. Logistic Regression only manages to achieve a below-average accuracy, signaling an average level of predictive quality. The Random Forest model fares slightly better with a slightly better accuracy rate, which implies that its ensemble method increases its predictive power to classify instances more effectively. In turn, the XG Boost model took the top spot in the comparison with an accuracy rate, projecting its ability to identify complex patterns in the data and showcasing the highest predictive level among the three models. The use of model outputs can greatly maximize real-time content strategy for brands and organizations seeking to maximize engagement in the USA. Based on user behavior patterns and engagement metrics, models can give insight into what should be posted and when to maximize attention. In campaign optimization, predictive modeling assists U.S. brands in making strategic decisions regarding ad spend allocation. Based on an examination of past performance and engagement metrics, brands can see which content is driving the greatest impact and engagement levels and make strategic investments in high-performance content that genuinely resonates with desired audiences. For public communication and policy, predictive models are particularly helpful in projecting how the U.S. public will react to news announcements, policy initiatives, or campaigns. Boosting the effectiveness of predictive models by incorporating transformer-based NLP models like BERT is one direction to explore in the future.