Classifying Problem and Solution Framing in Congressional Social Media

arXiv cs.CL / 4/7/2026

💬 OpinionIdeas & Deep AnalysisModels & Research

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Key Points

The paper investigates how U.S. Senators’ Twitter posts reflect the “Garbage Can” model by distinguishing between “problem”-focused and “solution”-focused policy processes.
Using a large dataset of 1.68M tweets, the authors build an automated labeling method where policy experts manually annotated 3,967 tweets into problem, solution, or other.
They train and evaluate supervised classifiers with a focus on F1 score, splitting the labeled data into training/validation/test subsets and iterating on model hyperparameters.
The best-performing approach fine-tunes a BERTweet Base model, achieving an average weighted F1 score above 0.8 across categories in cross-validation.

Abstract

Policy setting in the USA according to the ``Garbage Can'' model differentiates between ``problem'' and ``solution'' focused processes. In this paper, we study a large dataset of US Senator postings on Twitter (1.68m tweets in total). Our objective is to develop an automated method to label Senatorial posts as either in the problem or solution streams. Two academic policy experts labeled a subset of 3967 tweets as either problem, solution, or other (anything not problem or solution). We split off a subset of 500 tweets into a test set, with the remaining 3467 used for training. During development, this training set was further split by 60/20/20 proportions for fitting, validation, and development test sets. We investigated supervised learning methods for building problem/solution classifiers directly on the training set, evaluating their performance in terms of F1 score on the validation set, allowing us to rapidly iterate through models and hyperparameters, achieving an average weighted F1 score of above 0.8 on cross validation across the three categories using a BERTweet Base model.