Twitter has increasingly been used to study various research topics such as election predictions, disease spread, etc. However, social media platforms do not saturate the entire population in a study area, especially in emerging nations, only representing more affluent subpopulations. The U.S. Army Engineer Research and Development Center, Construction Engineering Research Laboratory (ERDC-CERL), as part of a project entitled Framework for the Integration of Complex Urban Systems (FICUS), is quantifying the utility of demographic information to inform neighborhood-scale social media models. Using the example topic of infrastructure, an open-source model was constructed to collect Twitter data from the metropolitan Philippines area of Manila, geotag tweets to neighborhood grid cells based on language analysis, and produce a sentiment topic map. ERDCs social media analysis tools incorporate quantifiable uncertainties with specific on-the-ground reporting techniques. By using the Humanitarian Crisis (HC) framework developed by PACOM (another FICUS product) as a model, a framework quantifying the likelihood of being a regular social media user was created to implement a data-driven, bottom-up framework construction nested within a knowledge-based established framework. This frame-work, and any other produced by the FICUS team serve as case studies for augmenting the military operational environment with quantifiable reduced uncertainties.