Understanding the Language of Political Agreement and Disagreement in Legislative Texts (2020)

Abstract: While national politics often receive the spotlight, the overwhelming majority of legislation proposed, discussed, and enacted is done at the state level. Despite this fact, there is little awareness of the dynamics that lead to adopting these policies. In this paper, we take the first step towards a better understanding of these processes and the underlying dynamics that shape them, using data-driven methods. We build a new large-scale dataset, from multiple data sources, connecting state bills and legislator information, geographical information about their districts, and donations and donors’ information. We suggest a novel task, predicting the legislative body’s vote breakdown for a given bill, according to different criteria of interest, such as gender, rural-urban and ideological splits. Finally, we suggest a shared relational embedding model, representing the interactions between the text of the bill and the legislative context in which it is presented. Our experiments show that providing this context helps improve the prediction over strong text-based models.

## introduction despite the fact that state-level legislation is rarely discussed, it has a dramatic influence on the everyday life of residents of the respective states. the policies enacted at the state-level touch on all aspects, from mundane topics, such as trash removal and state mascots, to highly ideologically-charged topics such as education, religious liberties, and health-care access. moreover, state-legislatures discuss and vote-on significantly more bills than their federal counterparts, adding up to over 120,000 bills per year @xcite . also, the lack of general interest, as well as the complexity of the processes that differ across states, often leads to public disengagement from local politics. this results in decisions being made with little understanding of the processes that shape them and how they are likely to influence different demographics. similarly, most effort directed at understanding political processes using data was directed at the federal level. in the nlp community, several works looked at analyzing political texts @xcite and the resulting behaviors of legislators @xcite . the only exception is recent work @xcite , predicting whether a bill would pass the preliminary stage, legislative committee, to a full-body vote. state-level demographic cleavages: our goal in this paper is to take a first step towards understanding the processes and interests that underlie how decisions are passed using data-driven methods. our main intuition is that the impact of bills on different demographics will be reflected in the behavior and voting patterns of their representatives. thus, providing the ability to automatically identify bills, before they are put to a vote, that will have a positive or negative influence on a specific demographic can help inform public responses and increase engagement with local political processes. to help achieve this goal, we define two novel text classification tasks, characterizing the breakdown of votes, based on different cleavages or demographic indicators such as gender, geography (i.e., rural vs. urban districts), party membership and ideological splits. with respect to each one of these splits, we define two aggregate-level proper-ties of a vote, competitive and inverse-competitive cleavages. both of these measures capture the lack of consensus in the legislature body around a specific bill, but in different ways. we say that a bill is competitive in a vote (fig. 1b ) if the majority of legislators from a logical group (e.g., democrats, women, urban districts, liberals) vote differently from the majority of legislators from the opposite group (e.g., republican, men, rural districts, conservatives). a bill is inverse-competitive (fig. 1c ) if there is a partial or complete tie within the legislators from the same group (e.g., women). to help explain these concepts, consider a bill restricting access to abortion clinics. this bill is likely to results in a competitive vote, based on ideology. on the other hand, a bill granting tax breaks for farmers might result in a inverse-competitive vote, based on ideology. in that case, a competitive vote, based on geography is more likely. in ## modeling we model the legislative process as a graph that consists of bills, legislators, and money donors in all states. building a global graph captures contextual information and relationships that interconnect different states. for instance, money donation by a contributor to two legislators from different states could indicate they have a similar roll call behaviors on abortion bills. given this intuition, after a brief overview of the legislative process in us states, we describe how we collapse it into a graph structure. ## inference on legislative graph we argue for a joint graph and text embedding model to represent the nodes and their textual attributes in the legislative graph, which is used for the roll-call prediction and aggregation. embedding models that only leverage textual information ignore important relations in the legislative graph. graph-based models make textual information less distinguishable at the classification stage, where it matters. at a high level, our approach combines the complementary strengths of both approaches. attributes biography cmte assignment [cls] sara is a member of… [sep] [cls] sara is served on … [sep] average legislator embedding vote aggregation relation prediction/classifier concatenation leg leg cont bill ffnn cleavage/ survival graph emb. text emb. r3 r2 r1 node encoder layers bill embed leg. embed con. embed [cls] emergency medications .sep] title description body [cls] the prescription of…[sep] [cls] a school nurse or… [sep] average bill embedding bert encoder (c) (a) [cls] at&t is a republican non-individual… [sep] [cls] at&t's specific economic category.. [sep] [cls] at&t's general economic category … [sep] average bert encoder bert encoder bert encoder contributor embedding general industry specific business attributes (d) (b) bert encoder bert encoder bert encoder bert encoder bert encoder our architecture (figure 4a ) uses bert's pretrained embedding to represent the textual information of nodes in the graph; and text-attributed rgcn to generate an embedding for them based on their relations. finally, we combine them to build a representation of edges in the graph for our relation prediction and then aggregate vote relations. ## experiments in this section, we describe our comprehensive legislative dataset, combining different sources of data (e.g., money donors data, diverse information on legislators).