  We disclose a methodology to determine the participants in discussions and
their contributions in social networks with a local relationship (e.g.,
nationality), providing certain levels of trust and efficiency in the process.
The dynamic is a challenge that has demanded studies and some approximations to
recent solutions. The study addressed the problem of identifying the
nationality of users in the Twitter social network before an opinion request
(of a political nature and social participation). The employed methodology
classifies, via machine learning, the Twitter users' nationality to carry out
opinion studies in three Central American countries. The Random Forests
algorithm is used to generate classification models with small training
samples, using exclusively numerical characteristics based on the number of
times that different interactions among users occur. When averaging the
proportions achieved by inferences of the ratio of nationals of each country,
in the initial data, an average of 77.40% was calculated, compared to 91.60%
averaged after applying the automatic classification model, an average increase
of 14.20%. In conclusion, it can be seen that the suggested set of method
provides a reasonable approach and efficiency in the face of opinion problems.
