论文标题
哪些账单游说？预测和解释美国的游说活动
Which bills are lobbied? Predicting and interpreting lobbying activity in the US
论文作者
论文摘要
使用OpenSecrets.org的游说数据，我们提供了一些应用机器学习技术的实验，以预测一项立法（美国法案）是否已进行游说活动。我们还研究了游说活动的强度对游说账单的可辨别法案的影响。我们比较了许多不同模型（逻辑回归，随机森林，CNN和LSTM）和文本嵌入表示形式（BOW，TF-IDF，Glove，Law2VEC）的性能。我们报告了ROC AUC评分高于0.85％的结果，精度为78％。当研究较高的游说强度时，模型性能会显着提高（95％的ROC AUC，精度为88％）。我们还提出了一种可用于未标记数据的方法。通过此，我们表明，有大量以前未标记的美国账单，我们的预测表明发生了一些游说活动。我们认为，我们的方法可能有助于通过指出可能受到游说的影响但没有提交的法案，从而有助于对美国游说披露法（LDA）的执行。
Using lobbying data from OpenSecrets.org, we offer several experiments applying machine learning techniques to predict if a piece of legislation (US bill) has been subjected to lobbying activities or not. We also investigate the influence of the intensity of the lobbying activity on how discernible a lobbied bill is from one that was not subject to lobbying. We compare the performance of a number of different models (logistic regression, random forest, CNN and LSTM) and text embedding representations (BOW, TF-IDF, GloVe, Law2Vec). We report results of above 0.85% ROC AUC scores, and 78% accuracy. Model performance significantly improves (95% ROC AUC, and 88% accuracy) when bills with higher lobbying intensity are looked at. We also propose a method that could be used for unlabelled data. Through this we show that there is a considerably large number of previously unlabelled US bills where our predictions suggest that some lobbying activity took place. We believe our method could potentially contribute to the enforcement of the US Lobbying Disclosure Act (LDA) by indicating the bills that were likely to have been affected by lobbying but were not filed as such.
