Predicting Media Impact: A Machine Learning Framework for Optimizing Corporate Communication Strategies in Architectural Practices - Abu-Shaikha, Ma’in; Nasereddin, Sara (International Journal of Computational and Experimental Science and Engineering, 2025)
The research investigates the role of media relations and corporate communications strategies of architectural firms that conventionally pursue PR methodologies and data-driven approaches have evolved. This has led to the conduct of research studies that use qualitative insights coupled with predictive modelling. These are used to examine how companies are evolving their communications approach in the digital age. This study investigates ten leading architecture firms, assessing communication effectiveness through qualitative interviews, media content analysis, and social media metrics. This further predicts the stakeholder engagement and media impact by applying machine learning models- Random Forest and LSTM networks with an accuracy of 85%. Key findings include that the drivers of engagement based on sentiment, content share ability, and media timing are significant. The study demonstrated how data-driven insights can drive strategic decision-making, optimize public relations, and improve stakeholder engagement. Moreover, the study provides an easily scalable framework for forecasting purposes in different markets. Further, it shows the promise of AI-driven communication strategies. Combining corporate communications theory with advanced analytics, this study shows how companies can benefit from the increasingly digital nature of media relations. This has been a major need for proactive reputation management and strategic content distribution. It enables architecture firms and others to better adapt to changing waves of media in response to maximal positive engagement.