Slums are among the most visible manifestation of urban poverty. In this vein, earth observation (EO) has been widely accepted as a tool to approximate associated socioeconomic disparities at the city level. In this work, we explore the potential of a novel data source - location-based social networks - in conjunction with EO-based slum maps. Applying meaningful location quotients for spatial clustering of digital hot and cold spots in an experimental setting, we find that such data can add generalized spatial knowledge to space-based methods via the designation of less digitally-oriented population groups. Conversely, slums derived from remote sensing show substantial quantitative correspondence with clustering results, and thus, even enable to reflect underlying intra-urban socioeconomic characteristics.