The use of social media, such as Twitter, has changed the information landscape for citizens participation in crisis response and recovery activities. Given that drought progression is slow and also spatially extensive, an interesting set of questions arise: How the usage of Twitter by a large population may change during the development of a major drought alongside how changing usage promotes drought detection? For this reason, by investigating contemporary procedures, this paper scrutinizes the potential to advance drought depiction. Hence, an analysis of how social media data, in conjunction with meteorological records, was conducted towards improvement in the detection of drought and furthermore, its progression. The research utilized machine learning techniques applied over satellite-derived drought conditions in Colorado. Specifically, 3 different machine learning techniques were examined: the generalized linear model, support vector machines and deep learning, each applied to test the integration of Twitter data with meteorological records as a predictor of drought development. It is maintained that the data integration of resources is viable given that the Twitter-based model outperformed the control run which did not include social media input. Furthermore the Twitter-based model was superior in predicting drought severity.