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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
An example of a House Bill is given here: https://www.congress.gov/bill/114th-congress/house-bill/3791/text.
- 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.
References
Aletras, N., Tsarapatsanis, D., Preoţiuc-Pietro, D., Lampos, V.: Predicting judicial decisions of the European court of human rights: a natural language processing perspective. PeerJ Comput. Sci. 2, e93 (2016)
Boella, G., Di Caro, L., Humphreys, L.: Using classification to support legal knowledge engineers in the Eunomos legal document management system. In: Fifth International Workshop on Juris-Informatics (JURISIN) (2011)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001). https://doi.org/10.1023/A:1010933404324
Chalkidis, I., Kampas, D.: Deep learning in law: early adaptation and legal word embeddings trained on large corpora. Artif. Intell. Law 27(2), 171–198 (2018). https://doi.org/10.1007/s10506-018-9238-9
Dale, R.: Law and word order: NLP in legal tech. Nat. Lang. Eng. 25(1), 211–217 (2019)
De Figueiredo, J.M., Richter, B.K.: Advancing the empirical research on lobbying. Ann. Rev. Polit. Sci. 17, 163–185 (2014)
Farzindar, A., Lapalme, G.: Legal text summarization by exploration of the thematic structure and argumentative roles. In: Text Summarization Branches Out, pp. 27–34 (2004)
Goldberg, Y.: Neural network methods for natural language processing. Synth. Lect. Hum. Lang. Technol. 10(1), 1–309 (2017)
Grasse, N., Heidbreder, B.: The influence of lobbying activity in state legislatures: evidence from Wisconsin. Legislative Stud. Q. 36(4), 567–589 (2011)
Grossmann, M., Pyle, K.: Lobbying and congressional bill advancement. Int. Groups Adv. 2(1), 91–111 (2013). https://doi.org/10.1057/iga.2012.18
Hachey, B., Grover, C.: Extractive summarisation of legal texts. Artif. Intell. Law 14(4), 305–345 (2006). https://doi.org/10.1007/s10506-007-9039-z
Hill, M.D., Kelly, G.W., Lockhart, G.B., Van Ness, R.A.: Determinants and effects of corporate lobbying. Financial Manag. 42(4), 931–957 (2013)
Laband, D.N., Sophocleus, J.P.: The social cost of rent-seeking: first estimates. Public Choice 58(3), 269–275 (1988). https://doi.org/10.1007/BF00155672
Li, P., Zhao, F., Li, Y., Zhu, Z.: Law text classification using semi-supervised convolutional neural networks. In: 2018 Chinese Control and Decision Conference (CCDC), pp. 309–313. IEEE (2018)
Lopez, R.A., Pagoulatos, E.: Rent seeking and the welfare cost of trade barriers. Public Choice 79(1–2), 149–160 (1994). https://doi.org/10.1007/BF01047924
Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: EMNLP (2014)
Sulea, O.-M., Zampieri, M., Vela, M., Van Genabith, J.: Predicting the law area and decisions of French supreme court cases. arXiv preprint arXiv:1708.01681 (2017)
Wongchaisuwat, P., Klabjan, D., McGinnis, J.O.: Predicting litigation likelihood and time to litigation for patents. In: Proceedings of the 16th Edition of the International Conference on Artificial Intelligence and Law, pp. 257–260. ACM (2017)
You, H.Y.: Ex post lobbying. J. Polit. 79(4), 1162–1176 (2017)
Acknowledgement
This work is supported by H2020 SoBigData++.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-59065-9_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-59064-2
Online ISBN: 978-3-030-59065-9
eBook Packages: Computer ScienceComputer Science (R0)