Legislative prediction with dual uncertainty minimization from heterogeneous information

Voting on legislative bills to form new laws serves as a key function of most legislature. Predicting the votes of such deliberative bodies leads to better understanding of government policies and generates actionable strategies for social good. In this paper, we present a novel prediction model that maximizes the usage of publicly accessible heterogeneous data, i.e., bill text and lawmakers' profile data, to carry out effective legislative prediction. In particular, we propose to design a probabilistic prediction model which achieves high consistency with past vote records while ensuring the minimum uncertainty of the vote prediction reflecting the firm legal ground often held by the lawmakers. In addition, the proposed legislative prediction model enjoys the following properties: inductive and analytical solution, abilities to deal with the prediction on new bills and new legislators, and robustness to the missing vote issue. We conduct extensive empirical study using the real legislative data and compare with other representative methods in both quantitative political science and data mining communities. The experimental results clearly corroborate that the proposed method provides superior prediction accuracy with visible performance gain.