用AI区分美国参议员推文是谈问题还是提方案。
Classifying Problem and Solution Framing in Congressional Social Media
- 基于BERTweet模型，自动识别推文属于问题或解决方案流
- 在3967条标注数据上实现超过0.8的加权F1分数
- 适合研究政治传播、政策分析的学者与从业者
根据“垃圾桶”政策模型，美国政策制定过程可区分出“问题”与“解决方案”两类流程。本文研究了总计168万条美国参议员在推特上的发文。目标是开发一种自动化方法，将参议员推文标记为“问题”或“解决方案”类别。两名学术政策专家对3967条推文进行了标注，分为“问题”、“解决方案”或“其他”（非前两者）。其中500条作为测试集，其余3467条用于训练。训练集进一步按60/20/20比例划分为拟合、验证与开发测试集。我们采用监督学习方法，在训练集上构建分类器，并以验证集上的F1分数为评估指标，快速迭代模型与超参数。最终使用BERTweet Base模型在交叉验证中实现三类别的平均加权F1分数超过0.8。
原文摘要 · Abstract (English)
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.
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