  Political speeches and debates play an important role in shaping the images
of politicians, and the public often relies on media outlets to select bits of
political communication from a large pool of utterances. It is an important
research question to understand what factors impact this selection process.
  To quantitatively explore the selection process, we build a three- decade
dataset of presidential debate transcripts and post-debate coverage. We first
examine the effect of wording and propose a binary classification framework
that controls for both the speaker and the debate situation. We find that
crowdworkers can only achieve an accuracy of 60% in this task, indicating that
media choices are not entirely obvious. Our classifiers outperform crowdworkers
on average, mainly in primary debates. We also compare important factors from
crowdworkers' free-form explanations with those from data-driven methods and
find interesting differences. Few crowdworkers mentioned that "context
matters", whereas our data show that well-quoted sentences are more distinct
from the previous utterance by the same speaker than less-quoted sentences.
Finally, we examine the aggregate effect of media preferences towards different
wordings to understand the extent of fragmentation among media outlets. By
analyzing a bipartite graph built from quoting behavior in our data, we observe
a decreasing trend in bipartisan coverage.
