Spatial prepositions such as in, on and near are important to describe where things are located in relation to other geographic features. Location-based services (LBS) usually disregard such spatial prepositions. Their automatic detec- tion and interpretation is challenging, because prepositions are quite often used in non-spatial context (e.g., “in the afternoon”). This paper analyses spatial relations in short messages. Short messages typically have special characteristics (e.g., slang, abbreviations, etc.) and thus represent a special type of natural language. A sample corpus of short messages was used to extract descriptions based on spatial prepo- sitions and to analyse their commonness of use. A frequency-based probability for each term to be spatial was calculated, which can serve as an indicator of a verbal spatial description and support the development of intelligent spatial language interpretation in automatic systems.