Geo-social media data involve various kinds of inhomogeneities. These can concern, amongst others, the users, but also spatial distributions or the fact that the most frequently used hashtags, keywords or emojis often have little relevance in the context under investigation. In order to properly tackle and reduce these inhomogeneities and to strive for a less distorted analysis, normalisation of geo-social media data is expedient. Various measures exist that are frequently used in research for this purpose. This paper presents four of these measures and compares them with each other, both theoretically as well as practically in the form of a demonstration through three exemplary case studies highlighting potentials and limitations of each measure. This comparison involves the relatively new typicality measure, which was developed specifically for this type of data following the dimensions commonly used to describe geo-social media data (temporal, spatial, social and thematic dimension).