In the response processes to the disasters given by the floods in Bogotá Colombia; the use of information derived from Big Data generated in social networks, known as unstructured data, in the sub-direction of emergency and disaster management of the District Institute for Risk Management and Climate Change (IDIGER); It will support institutional responses, understood as: decision-making, attention times and adequate resource management through the prioritization of affected areas, through the collection, processing and analysis of information obtained quickly and effectively. The following research, aims to determine how the use of information obtained from the data generated from social networks, is useful to prioritize affected areas and support decision-making within IDIGER institutional responses to disasters generated by floods, through of the identification of the actions developed during the attention to an emergency; a bibliographic review of the methodology and procedures within application models on the use of information, which compares such actions with projects carried out in other countries, as well as the proposal of a methodological guide that proposes options to analyze the information obtained, focusing on the determination and cartographic representation of areas affected by flood disasters; prioritizing these areas and supporting institutional responses. The foregoing results are: a bibliographic review of the methodologies and procedures within application models of the use of information obtained from the data generated in social networks, against the handling of disasters caused by floods, and their respective comparative analysis; a diagnosis of the activities carried out within the sub-direction of emergency and disaster management of IDIGER to address a flood emergency, and the formulation of a proposal consisting of a methodological guide focused on the prioritization of areas affected by floods, which makes use of cartographic tools, as well as methods of extracting information directly from the data generated in social networks and the analysis of said information to support decision-making in institutional responses