  The term idiolect refers to the unique and distinctive use of language of an
individual and it is the theoretical foundation of Authorship Attribution. In
this paper we are focusing on learning distributed representations (embeddings)
of social media users that reflect their writing style. These representations
can be considered as stylistic fingerprints of the authors. We are exploring
the performance of the two main flavours of distributed representations, namely
embeddings produced by Neural Probabilistic Language models (such as word2vec)
and matrix factorization (such as GloVe).
