## 1 Introduction  
The current dynamic nature of cities has generated substantial changes in urban environments, making the analysis of geo-spatial information more complex [1]. The urban management and planning of cities has been nourished for years by Geographic Information Systems (GIS) tools, since they allow storing, modeling, and analyzing geo-spatial data. On the other hand, urban planning faces challenges such as organizational changes, staff availability, updating resources, and data availability [2]. In this context, land use maps are one of the most demanded resources by authorities and researchers, for conducting urban planning and environmental sustainability studies [3]. However, the production of these maps is costly and time-consuming [4]. Consequently, the production and management of land use maps evidence a technical problem [5] caused by economic constraints, mostly in developing countries. The Internet generates large volumes of data at a high rate, in particular, posts on social networks. Social networks have become a source of data for researchers, because these data contain information that allows studying the interaction of citizens with their environment [6]. The concept of \"user-generated content\" [7, 8], which encompasses how citizens share excerpts from their daily lives, can be translated to different application domains: the representation of urban boundaries, relationships between weather conditions and traffic, citizen's activity patterns, urban transportation behavior and land use [6; 9], urban planning, and spatial planning. Therefore, social networks are a valuable source of data that could be used to identify interesting aspects of a city's land use. Although social network data have numerous semantic adulterations, and are not intended to be a source of geo-spatial information, in the text of posts we find pieces of important information about how people interact with their space, and can complement other sources in the process of identifying land uses [10]. These data can be composed of various fields such as message text, location, and images. In this context, various approaches have been developed and applied to analyze this data type; the semantic analysis of the text of the messages--unstructured information--being the basis of much of the research related to social networks. However, it is an underexploited resource in obtaining and analyzing geographic information [11]. Current land use identification and analysis studies rely primarily on publication metadata, such as time and geographical coordinates--structured information. The various studies on land use classification have rarely considered the content of posts as a source of data to infer the type of use. In the content of social network posts, people communicate attitudes, activities, relationships, and other emotional states that are often complemented with a geo-spatial location. In written expressions it is possible to find words that allow inferring information about where a user is situated. For example, if a person posts the following message \"having lunch with friends\" on a social network, we could assume that they are in a restaurant, a house, a shopping mall, or another place where they can have lunch. In addition, if we associate two or more words such as \"having lunch with friends in a restaurant\", the connector \"in\" and the word \"restaurant\" provide us with a high probability that the place where he/she is located is, in fact, a restaurant. If the coordinates associated with the message are included, we can know the space where the restaurant is placed, and consequently, the type of land use to which that space belongs. Therefore, the potential of the content of social network publications to address the study of land use is remarkable. Nevertheless, the language used in social network publications is very informal, includes many idioms, expressions specific to each region, misspellings, or other language deformations.