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Which Bills Are Lobbied? Predicting and Interpreting Lobbying Activity in the US

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Big Data Analytics and Knowledge Discovery (DaWaK 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12393))

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Abstract

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.

We thank the Center for Responsive Politics (OpenSecrets.org) for making their lobbying data available.

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Notes

  1. 1.

    An example of a House Bill is given here: https://www.congress.gov/bill/114th-congress/house-bill/3791/text.

  2. 2.

    In the US, lobbying activities (above a certain threshold) need to be disclosed, and non-compliance can result in a pecuniary sanction (fine) or, in some cases up to 5 years imprisonment. In Sect. 5 we revisit this assumption.

  3. 3.

    https://www.hklaw.com/en/insights/publications/2017/11/what-is-the-lobbying-disclosure-act-lda.

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Acknowledgement

This work is supported by H2020 SoBigData++.

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Correspondence to Peter Ormosi .

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Slobozhan, I., Ormosi, P., Sharma, R. (2020). Which Bills Are Lobbied? Predicting and Interpreting Lobbying Activity in the US. In: Song, M., Song, IY., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2020. Lecture Notes in Computer Science(), vol 12393. Springer, Cham. https://doi.org/10.1007/978-3-030-59065-9_23

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  • DOI: https://doi.org/10.1007/978-3-030-59065-9_23

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