Social media has become a central source of data for both academia and industry. However, access to this data is restricted by official API limitations, opaque commercial solutions, and fragmented scraping tools. This article presents a framework design for collecting, homogenizing, and storing cross-platform social media data. The design combines automated collection with structured extraction of text, images, videos, audio, metadata, URLs, and comments, organizing these elements into a standardized database. This standardization reduces the engineering effort required to consolidate data, facilitating comparative analyses across diverse social networks with different processing methods. In addition, the design foresees time-based controls, logging, and management of multiple accounts and cookies, making the collection process more robust in the face of account blocking, access limits, and changes to social network platforms.