  Information about individuals can help to better understand what they say,
particularly in social media where texts are short. Current approaches to
modelling social media users pay attention to their social connections, but
exploit this information in a static way, treating all connections uniformly.
This ignores the fact, well known in sociolinguistics, that an individual may
be part of several communities which are not equally relevant in all
communicative situations. We present a model based on Graph Attention Networks
that captures this observation. It dynamically explores the social graph of a
user, computes a user representation given the most relevant connections for a
target task, and combines it with linguistic information to make a prediction.
We apply our model to three different tasks, evaluate it against alternative
models, and analyse the results extensively, showing that it significantly
outperforms other current methods.
