  When discussing a tweet, people usually not only refer to the content it
delivers, but also to the person behind the tweet. In other words, grounding
the interpretation of the tweet in the context of its creator plays an
important role in deciphering the true intent and the importance of the tweet.
  In this paper, we attempt to answer the question of how creator context
should be used to advance tweet understanding. Specifically, we investigate the
usefulness of different types of creator context, and examine different model
structures for incorporating creator context in tweet modeling. We evaluate our
tweet understanding models on a practical use case -- recommending relevant
tweets to news articles. This use case already exists in popular news apps, and
can also serve as a useful assistive tool for journalists. We discover that
creator context is essential for tweet understanding, and can improve
application metrics by a large margin. However, we also observe that not all
creator contexts are equal. Creator context can be time sensitive and noisy.
Careful creator context selection and deliberate model structure design play an
important role in creator context effectiveness.
