  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.
