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

You Shall Know a User by the Company It Keeps: Dynamic Representations for Social Media Users in NLP