## 4 Conclusions  
Social networks provide valuable data on urban dynamics, offering new opportunities for research in the field. In this study, we used Twitter data to analyze land use in the historical center of the city of Arequipa by capturing tweets from the area with the text of the publication, time, date, and coordinates.  
This research proposes a complete methodology of NLP for the analysis of tweet texts and coordinates, together with the Naive Bayes Multinomial algorithm for the classification of spaces within land use categories. The evaluation of the model shows that the approach provides excellent results, with accuracy of about 90%, and F1-score of about 88%. The information of the area obtained from the project \"Height for Culture\" is used as a basis for the interpretation of the results, verifying that, as expected due to the knowledge of the area, a large percentage of the properties belong to the commercial category because these are in the historical city center with high presence of tourism, followed by the buildings of the institutional-cultural category due to the historical character of the area. However, the methodology detected that many residential spaces registered in the cadastre have currently other activities or uses.  
We conclude that the Twitter data provides useful information to identify land uses in the geographic area where it is captured. Tweets are gathered in a simple and inexpensive way and provide information that can be used as an additional method by urban planning professionals and organizations interested in that area.The advantage of this model over the traditional ones resides in its dynamism since it uses data that is constantly updated by the users and allows reflecting the inconsistencies that exist in the maps generated by the cadastre due to the constant change of the environment. Moreover, the methodology is easily transferred to other geographical areas by means of the use of specific dictionaries to the region under study. This knowledge might be used as a recommendation system for short-term supervision or updating of the cadastre.  
Finally, this method depends on the amount of geo-located data available at the time of classification, so the capture of publications from other social networks should be considered for future work to maximize the effectiveness and usefulness of the results.  
Conceptualization, D.C.P.-Q. and E.S.-L.; methodology, D.C.P.-Q. and J.S.A.-R.; software, D.C.P.-Q. and K.A.-T.; validation, C.B.-R. and K.A.-T.; resources, C.B.-R.; writing--original draft preparation, D.C.P.-Q., J.S.A.-R. and E.S.-L. All authors have read and agreed to the published version of the manuscript.  
Figure 7: The highlighted area corresponds to the convert of Santa Catalina (in the cadastre it is wrongly classified as residential land use).  
**Funding:** This work was supported by the Andalusian Plan for Research, Development and Innovation, by Grant PID2020-117759 GB-I00 funded by MCIN/AEI/10.13039/501100011033, Spain, and by Grant IBA0021-2017-UNSA funded by the Universidad Nacional de San Agustin de Arequipa, Peru.  
**Conflicts of Interest:** The authors declare no conflict of interest.