  Your name tells a lot about you: your gender, ethnicity and so on. It has
been shown that name embeddings are more effective in representing names than
traditional substring features. However, our previous name embedding model is
trained on private email data and are not publicly accessible. In this paper,
we explore learning name embeddings from public Twitter data. We argue that
Twitter embeddings have two key advantages: \textit{(i)} they can and will be
publicly released to support research community. \textit{(ii)} even with a
smaller training corpus, Twitter embeddings achieve similar performances on
multiple tasks comparing to email embeddings.
  As a test case to show the power of name embeddings, we investigate the
modeling of lifespans. We find it interesting that adding name embeddings can
further improve the performances of models using demographic features, which
are traditionally used for lifespan modeling. Through residual analysis, we
observe that fine-grained groups (potentially reflecting socioeconomic status)
are the latent contributing factors encoded in name embeddings. These were
previously hidden to demographic models, and may help to enhance the predictive
power of a wide class of research studies.
