Data Warehouse Design to Support Social Media Analysis: The Case of Twitter and Facebook

The proliferation of data generated and stored through social media has experienced a significant surge over the past decade. Consequently, the analysis and interpretation of such data have emerged as valuable sources of insights across diverse contexts, serving as aids for researchers and businesses in making informed decisions. However, the data is widespread, stemming from diverse sources with distinct formats, and is generated at a rapid pace. These characteristics collectively contribute to the intricacy of extracting knowledge from this data, transforming the process into one that is both complex and resource-intensive. The central scientific contribution of this paper lies in the formulation of a social media data integration model, built upon the foundation of a data warehouse. This model is designed to alleviate the computational costs associated with data analysis while concurrently facilitating the application of techniques aimed at discovering meaningful insights. Notably, this study differentiates itself from existing literature by concentrating on both the Facebook and Twitter social media platforms. Additionally, we introduce a model that covers data acquisition, transformation, and loading processes, enabling the extraction of valuable insights even when the data’s complexity surpasses human understanding.