  Predicting the geographical location of users of social media like Twitter
has found several applications in health surveillance, emergency monitoring,
content personalization, and social studies in general. In this work we
contribute to the research in this area by designing and evaluating new methods
based on the literature of weighted multigraphs combined with state-of-the-art
deep learning techniques. The explored methods depart from a similar underlying
structure (that of an extended mention and/or follower network) but use
different information processing strategies, e.g., information diffusion
through transductive and inductive algorithms -- RGCNs and GraphSAGE,
respectively -- and node embeddings with Node2vec+. These graphs are then
combined with attention mechanisms to incorporate the users' text view into the
models. We assess the performance of each of these methods and compare them to
baseline models in the publicly available Twitter-US dataset; we also make a
new dataset available based on a large Twitter capture in Latin America.
Finally, our work discusses the limitations and validity of the comparisons
among methods in the context of different label definitions and metrics.
