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

Designing weighted and multiplex networks for deep learning user geolocation in Twitter