  When writing, a person may need to anticipate questions from their audience,
but different social groups may ask very different types of questions. If
someone is writing about a problem they want to resolve, what kind of follow-up
question will a domain expert ask, and could the writer better address the
expert's information needs by rewriting their original post? In this paper, we
explore the task of socially-aware question generation. We collect a data set
of questions and posts from social media, including background information
about the question-askers' social groups. We find that different social groups,
such as experts and novices, consistently ask different types of questions. We
train several text-generation models that incorporate social information, and
we find that a discrete social-representation model outperforms the text-only
model when different social groups ask highly different questions from one
another. Our work provides a framework for developing text generation models
that can help writers anticipate the information expectations of highly
different social groups.
