  Existing sarcasm detection systems focus on exploiting linguistic markers,
context, or user-level priors. However, social studies suggest that the
relationship between the author and the audience can be equally relevant for
the sarcasm usage and interpretation. In this work, we propose a framework
jointly leveraging (1) a user context from their historical tweets together
with (2) the social information from a user's conversational neighborhood in an
interaction graph, to contextualize the interpretation of the post. We use
graph attention networks (GAT) over users and tweets in a conversation thread,
combined with dense user history representations. Apart from achieving
state-of-the-art results on the recently published dataset of 19k Twitter users
with 30K labeled tweets, adding 10M unlabeled tweets as context, our results
indicate that the model contributes to interpreting the sarcastic intentions of
an author more than to predicting the sarcasm perception by others.
