  The appearance of complex attention-based language models such as BERT,
Roberta or GPT-3 has allowed to address highly complex tasks in a plethora of
scenarios. However, when applied to specific domains, these models encounter
considerable difficulties. This is the case of Social Networks such as Twitter,
an ever-changing stream of information written with informal and complex
language, where each message requires careful evaluation to be understood even
by humans given the important role that context plays. Addressing tasks in this
domain through Natural Language Processing involves severe challenges. When
powerful state-of-the-art multilingual language models are applied to this
scenario, language specific nuances use to get lost in translation. To face
these challenges we present \textbf{BERTuit}, the larger transformer proposed
so far for Spanish language, pre-trained on a massive dataset of 230M Spanish
tweets using RoBERTa optimization. Our motivation is to provide a powerful
resource to better understand Spanish Twitter and to be used on applications
focused on this social network, with special emphasis on solutions devoted to
tackle the spreading of misinformation in this platform. BERTuit is evaluated
on several tasks and compared against M-BERT, XLM-RoBERTa and XLM-T, very
competitive multilingual transformers. The utility of our approach is shown
with applications, in this case: a zero-shot methodology to visualize groups of
hoaxes and profiling authors spreading disinformation.
  Misinformation spreads wildly on platforms such as Twitter in languages other
than English, meaning performance of transformers may suffer when transferred
outside English speaking communities.
