AI Chat Paper
Discover the SciOpen Platform and Achieve Your Research Goals with Ease.
Search articles, authors, keywords, DOl and etc.
{{expandStatus?'Exit ':''}}Advanced Search
Common legislative prediction methods often emphasize bill content or social relationships. This paper, motivated by the insight that similar policy texts reflect comparable political ideologies and can lead to similar voting outcomes, proposes a deep learning method that exploits attention mechanisms to incorporate semantic similarity between bills into legislative prediction models. Our approach uses attention scores to identify bills that are most similar to the one being predicted, and combines the encoded features of these similar bills as additional auxiliary information. By integrating these related features, the model goes beyond the semantic information of individual bills, leading to a more comprehensive use of roll-call data. Empirical results show that utilizing bill similarity along with traditional social relationships, voter characteristics, and bill content significantly improves performance in terms of accuracy, recall, precision, and F1 score compared to models that ignore bill similarity. The results also confirm that legislators tend to maintain consistent views or voting patterns on bills that are similar in nature. In addition, we demonstrate that the attention mechanism is more effective than conventional similarity measures, such as cosine similarity and Euclidean distance, in capturing the similarities between bills.
S. Jackman, Multidimensional analysis of roll-call data via Bayesian simulation: Identification, estimation, inference, and model checking, Political Analysis, vol. 9, no. 3, pp. 227–241, 2001.
K. Miler, Legislative responsiveness to constituency change, Am. Polit. Res., vol. 44, no. 5, pp. 816–843, 2016.
B. Lee, M. Pomirchy, and B. Schonfeld, Does the U.S. Congress respond to public opinion on trade, Am. Polit. Res., vol. 51, no. 6, pp. 731–748, 2023.
C. Ainsley, C. J. Carrubba, B. F. Crisp, B. Demirkaya, M. J. Gabel, and D. Hadzic, Roll-call vote selection: Implications for the study of legislative politics, Am. Polit. Sci. Rev., vol. 114, no. 3, pp. 691–706, 2020.
J. J. Nay, Predicting and understanding law-making with word vectors and an ensemble model, PLoS One, vol. 12, no. 5, p. e0176999, 2017.
J. Clinton, S. Jackman, and D. Rivers, The statistical analysis of roll-call data, American Journal of Political Science, vol. 98, no. 2, pp. 355–370, 2004.
S. Wojcik, Do birds of a feather vote together, or is it peer influence, Polit. Res. Q., vol. 71, no. 1, pp. 75–87, 2018.
M. Laver, K. Benoit, and J. Garry, Extracting policy positions from political texts using words as data, Am. Pol. Sci. Rev., vol. 97, no. 2, pp. 311–331, 2003.
R. M. Entman, Framing: Toward clarification of a fractured paradigm, J. Commun., vol. 43, no. 4, pp. 51–58, 1993.
A. Born and A. Janssen, Does a district mandate matter for the behavior of politicians? An analysis of roll-call votes and parliamentary speeches, Eur. J. Polit. Econ., vol. 71, p. 102070, 2022.
J. H. Kim, G. A. Barnett, and K. H. Kwon, The influence of social networks on the U.S. senate roll-call voting, Int. J. E Polit., vol. 1, no. 4, pp. 22–41, 2010.
J. H. Fowler, Connecting the congress: A study of cosponsorship networks, Polit. Anal., vol. 14, no. 4, pp. 456–487, 2006.
J. H. Fowler, Legislative cosponsorship networks in the US house and senate, Soc. Netw., vol. 28, no. 4, pp. 454–465, 2006.
A. J. Clarke, Party sub-brands and American party factions, Am. J. Polit. Sci., vol. 64, no. 3, pp. 452–470, 2020.
J. M. Snyder and T. Groseclose, Estimating party influence in congressional roll-call voting, Am. J. Polit. Sci., vol. 44, no. 2, pp. 193–211, 2000.
P. Clerici, Committee collaboration, competing principals, and partisanship in Argentina, The Journal of Legislative Studies, vol. 31, no. 1, pp. 248–269, 2023.
E. Salazar, D. B. Dunson, and L. Carin, Analysis of space–time relational data with application to legislative voting, Comput. Stat. Data Anal., vol. 68, pp. 141–154, 2013.
D. Cherepnalkoski, A. Karpf, I. Mozetič, and M. Grčar, Cohesion and coalition formation in the European parliament: Roll-call votes and twitter activities, PLoS One, vol. 11, no. 11, p. e0166586, 2016.
D. Schoch and U. Brandes, Legislators’ roll-call voting behavior increasingly corresponds to intervals in the political spectrum, Sci. Rep., vol. 10, no. 1, p. 17369, 2020.
G. Koger, S. Masket, and H. Noel, Cooperative party factions in American politics, Am. Polit. Res., vol. 38, no. 1, pp. 33–53, 2010.
M. Fischer, F. Varone, R. Gava, and P. Sciarini, How MPs ties to interest groups matter for legislative co-sponsorship, Soc. Netw., vol. 57, pp. 34–42, 2019.
Z. Khashman and A. Khashman, Anticipation of political party voting using artificial intelligence, Procedia Comput. Sci., vol. 102, pp. 611–616, 2016.
S. Sengupta and V. Dave, Predicting applicable law sections from judicial case reports using legislative text analysis with machine learning, J. Comput. Soc. Sci., vol. 5, no. 1, pp. 503–516, 2022.
H. Mentzingen, N. António, F. Bacao, and M. Cunha, Textual similarity for legal precedents discovery: Assessing the performance of machine learning techniques in an administrative court, Int. J. Inf. Manag. Data Insights, vol. 4, no. 2, p. 100247, 2024.
The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
京公网安备11010802044758号