is most suitable when monitoring is done for a specific region, and high precision is required from geolocation. Details on this approach are given in section \[isv2018:success:pol\].

Approach (ii) allows Talaia to analyze the differences in opinions with respect to a topic that may arise between regions or countries. Geolocation is done by exploiting social media information from both messages and authors. If a message is geolocated, its information is used straightforwardly. Otherwise, user profile information is analyzed. The task is challenging, because users do not provide such information always (40%-45% of the users in our datasets have location information), or they define fictitious locations (e.g., ‘Middle earth’, ‘In a galaxy far, far away...’; 14%). Several geocoding APIs[^19] are queried, and results are then weighted, because APIs show divergent results when feeding fictitious locations. The weighted system obtains 82% accuracy for those users containing location information in their profile. Roughly, we are able to geolocate correctly around 32% of the users that appear in a monitoring process.

Gender detection is another important factor in many social science studies. A supervised gender classifier is implemented to infer user gender, based on features extracted from academic papers [@kokkos2014robust; @rangel2017overview]. User gender detection 