哪些议案被游说？预测和解释US中的游说活动
Which bills are lobbied? Predicting and interpreting lobbying activity in the US
摘要：
使用来自OpenSecrets.org的游说数据，我们提供了几个应用机器学习技术的实验，以预测一项立法（美国法案）是否受到游说活动的影响。我们还调查了游说活动的强度的影响如何辨别游说法案是从一个不受游说。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（BOW，TF-IDF，GloVe，Law 2 Vec）的性能。我们报告的结果超过0.85%ROC AUC评分，准确率为78%。当研究游说强度较高的法案时，模型性能显著提高（95% ROC AUC和88%准确度）。我们还提出了一种方法，可用于未标记的数据。通过这一点，我们表明，有相当数量的以前未标记的美国法案，我们的预测表明，一些游说活动发生。我们相信，我们的方法可能有助于美国游说披露法（LDA）的执行，指出可能受到游说影响但没有提交的法案。
英文摘要：
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.
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