  Citations are an important indicator of the state of a scientific field,
reflecting how authors frame their work, and influencing uptake by future
scholars. However, our understanding of citation behavior has been limited to
small-scale manual citation analysis. We perform the largest behavioral study
of citations to date, analyzing how citations are both framed and taken up by
scholars in one entire field: natural language processing. We introduce a new
dataset of nearly 2,000 citations annotated for function and centrality, and
use it to develop a state-of-the-art classifier and label the entire ACL
Reference Corpus. We then study how citations are framed by authors and use
both papers and online traces to track how citations are followed by readers.
We demonstrate that authors are sensitive to discourse structure and
publication venue when citing, that online readers follow temporal links to
previous and future work rather than methodological links, and that how a paper
cites related work is predictive of its citation count. Finally, we use changes
in citation roles to show that the field of NLP is undergoing a significant
increase in consensus.
