Which bills are lobbied? Predicting and interpreting lobbying activity in the USWe thank the Center for Responsive Politics (OpenSecrets.org) for making their lobbying data available.
Table 4: Classification results - results for our three labels
['Model', 'Validation AUC ROC', 'Validation ACC.', 'Test AUC ROC', 'Test ACC.']
We denoted these as Labelling 1, 2, and 3. In each of these three versions of the dataset, we split the data into train, validation, and test sets with proportions of 72% for train, 8% for validation, and 20% for test, respectively. It appears that the best performing model and feature sets are logistic regressions with TF-IDF, and LSTM with GloVe embeddings. Regarding the word embedding representations, we TF-IDF provides the best AUC ROC and accuracy on the test sample. Interestingly, Law2Vec is slightly outperformed by both GloVe and TF-IDF. Comparison of the three sets of results clearly indicates that the prediction improves as we re-define our label in terms of lobbying intensity. This provides the worst results. This is in line with intuition: it is likely that bills that were lobbied only once are not hugely different from those that were not lobbied at all (for example, the lobbying might have been for a benign, minor correction of the text, or the lobbying might have not successfully changed the text of the legislation at all). the results show improvement. In these experiments we compared bills that were not lobbied with bills that were lobbied intensively, at least 10, and at least 50 times respectively. The results suggest that the difference between the text of lobbied and non-lobbied bills becomes more discernible where there is more intensive lobbying. Put differently, a bill that was not lobbied is more similar to a bill that was lobbied only once, than to a bill that was lobbied, say, 20 times.