New Dataset Reveals Systematic Differences in Congressional Questioning Strategies by Party
Researchers have released C-QUERI, a dataset of question-answer pairs extracted from U.S. congressional committee hearings spanning the 108th through 117th Congress. The dataset was built using a pipeline that processes unstructured hearing transcripts, taking advantage of the formal structure of congressional hearings to enable cross-party comparisons. The work reveals systematic partisan differences in questioning strategies, finding that a questioner's party affiliation can be predicted from their questions alone.
A team of researchers has introduced C-QUERI (Congressional Questions, Exchanges, and Responses in Institutions), a large-scale dataset drawn from U.S. congressional committee hearings covering roughly two decades of legislative activity. The dataset was constructed using an automated pipeline designed to extract structured question-answer pairs from raw, unstructured hearing transcripts. Congressional hearings were chosen as the study site because their formal rules, guaranteed participation across party lines, and witness obligations to respond make them an unusually tractable environment for studying political discourse. Analysis of the dataset demonstrates that systematic differences exist in how members of different parties frame and deploy questions, to the point where machine learning models can predict a questioner's party affiliation from the text of their questions alone. The authors argue this finding supports the view that political questioning serves strategic purposes beyond simple information gathering, including advancing partisan narratives and shaping public perception. Beyond congressional politics, the researchers present their pipeline as a general framework applicable to other interview-like settings. The paper was submitted to arXiv in September 2025 and revised in June 2026.
What's missing
The paper does not detail which specific NLP or classification models were used to predict party affiliation, nor does it report accuracy metrics or baselines for that prediction task in the abstract. It is also unclear whether the dataset accounts for changes in committee composition, majority/minority status shifts across Congresses, or topic-driven confounds that might independently explain partisan language differences. The generalizability of the pipeline to non-English or less formally structured legislative settings is not addressed.
What different sources said
- arXiv cs.CLCenter
C-QUERI: Congressional Questions, Exchanges, and Responses in Institutions Dataset
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