As the world’s climate changes, species are undergoing range shifts. Range shifts are generally documented using databases such as the Global Biodiversity Information Facility (GBIF), which largely contain data from monitoring schemes and wildlife surveys. Such databases have two major limitations: i) data may be spatially biased because traditionally surveyed areas are in rural habitats, ii) there is a time lag between data collection and assimilation into GBIF, which means rapid range shifts cannot be tracked. Alternative data sources, such as social media, could provide information on species distributions and range shifts that compensate for spatial biases in GBIF records because social media data may be collected outside traditional surveyed areas. Such data are also usually shared online immediately after a wildlife sighting. The complementarity of GBIF and social media data, however, has rarely been assessed, particularly when tracking range shifts. Despite their potential utility, social media data may be particularly prone to temporary trends or geographic variation in behaviour that are not understood. We lack tools with which to counter these biases. To address these knowledge gaps, we compare the habitat usage revealed by biological records of the Jersey tiger moth from GBIF and from multiple social media data sources (Instagram, iNaturalist, and Flickr). We develop a novel method to account for recorder bias in social media data. We find that biological records from iNaturalist and Instagram reveal greater than expected occurrence in urban environments, greatly affecting the accuracy of habitat suitability models. We also develop a method for comparing recorder effort between multiple data sources. Recorder effort differs notably between data sources, and Instagram complements GBIF by recording species in areas unaccounted for by GBIF. By incorporating recorder effort metrics, data from social media sources could be used to improve monitoring of range shifting species in urban spaces.