Methodological proposal to identify the nationality of Twitter users through Random-Forests

TL;DR

This paper presents a machine learning methodology using Random Forests to identify Twitter users' nationality with high accuracy, aiding opinion studies in Central American countries.

Contribution

The study introduces a novel approach that classifies user nationality using only numerical interaction features with small training samples.

Findings

Achieved an average accuracy increase of 14.20% after applying the model.

Demonstrated effectiveness of the method in social participation and political opinion analysis.

Provided a feasible solution for nationality inference with limited data.

Abstract

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.