New Dataset Reveals Systematic Differences in Congressional Questioning Strategies by Party
Researchers have created C-QUERI, a large-scale dataset of question-answer pairs extracted from congressional hearing transcripts spanning the 108th through 117th Congress. The dataset enables analysis of how members of Congress from different parties employ distinct questioning strategies to advance partisan narratives and shape public perception. The work provides tools for studying political discourse patterns and demonstrates that a questioner's party affiliation can be predicted from their questions alone.
A team of computer science researchers has developed C-QUERI, a novel dataset and extraction pipeline designed to analyze strategic questioning in congressional hearings. The dataset contains question-answer pairs from committee hearings across a decade of Congress, capturing interactions where formal rules structure exchanges and witnesses are obliged to respond. Analysis of the dataset reveals systematic differences in questioning strategies across political parties, with the researchers demonstrating that machine learning models can predict a questioner's party affiliation based solely on their questions. Beyond congressional politics, the researchers present their work as a general framework applicable to analyzing question-answering dynamics in other interview-like settings. The study addresses a gap in research on how political questions serve purposes beyond information gathering, including advancing partisan narratives and shaping public perceptions.
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- arXiv cs.CLCenter
C-QUERI: Congressional Questions, Exchanges, and Responses in Institutions Dataset
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