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.

本文研究政治沟通、总统辩论、媒体报道、二元分类和引用行为，揭示媒体选择哪些政治言论进行报道的因素及对政治人物形象的影响，数据分析发现媒体选择与底层因素不一致，呈现分歧趋势。

媒体对总统辩论中精华片段的选择：“你不是杰克·肯尼迪