[2604.03247] Classifying Problem and Solution Framing in Congressional Social Media

Computer Science > Computers and Society arXiv:2604.03247 (cs) [Submitted on 10 Mar 2026] Title:Classifying Problem and Solution Framing in Congressional Social Media Authors:Misha Melnyk, Mitchell Dolny, Joshua D. Elkind, A. Michael Tjhin, Saisha Chebium, Blake VanBerlo, Annelise Russell, Michelle M. Buehlmann, Jesse Hoey View a PDF of the paper titled Classifying Problem and Solution Framing in Congressional Social Media, by Misha Melnyk and 8 other authors View PDF HTML (experimental) 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 w...