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

Deriving Disinformation Insights from Geolocalized Twitter Callouts