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Using lobbying data from OpenSecrets.org, we offer several experimentsapplying machine learning techniques to predict if a piece of legislation (USbill) has been subjected to lobbying activities or not. We also investigate theinfluence of the intensity of the lobbying activity on how discernible alobbied bill is from one that was not subject to lobbying. We compare theperformance of a number of different models (logistic regression, randomforest, 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) whenbills with higher lobbying intensity are looked at. We also propose a methodthat could be used for unlabelled data. Through this we show that there is aconsiderably large number of previously unlabelled US bills where ourpredictions suggest that some lobbying activity took place. We believe ourmethod could potentially contribute to the enforcement of the US LobbyingDisclosure Act (LDA) by indicating the bills that were likely to have beenaffected by lobbying but were not filed as such.

Which bills are lobbied? Predicting and interpreting lobbying activity in the US