  This paper demonstrates a two-stage method for deriving insights from social
media data relating to disinformation by applying a combination of geospatial
classification and embedding-based language modelling across multiple
languages. In particular, the analysis in centered on Twitter and
disinformation for three European languages: English, French and Spanish.
Firstly, Twitter data is classified into European and non-European sets using
BERT. Secondly, Word2vec is applied to the classified texts resulting in
Eurocentric, non-Eurocentric and global representations of the data for the
three target languages. This comparative analysis demonstrates not only the
efficacy of the classification method but also highlights geographic, temporal
and linguistic differences in the disinformation-related media. Thus, the
contributions of the work are threefold: (i) a novel language-independent
transformer-based geolocation method; (ii) an analytical approach that exploits
lexical specificity and word embeddings to interrogate user-generated content;
and (iii) a dataset of 36 million disinformation related tweets in English,
French and Spanish.
