We present a probabilistic model of the influence of language on the behavior of the U.S. Supreme Court, specifically influence of amicus briefs on Court decisions and opinions.The approach assumes that amici are rational, utility-maximizing agents who try to win votes or affect the language of court opinions.Our model leads to improved predictions of justices' votes and perplexity of opinion language.It is amenable to inspection, allowing us to explore inferences about the persuasiveness of different amici and influenceability of different justices; these are consistent with earlier findings."Language is the central tool of our trade."

We explore the idea that authoring a piece of text is an act of maximizing one's expected utility.To make this idea concrete, we consider the societally important decisions of the Supreme Court of the United States.Extensive past work in quantitative political science provides a framework for empirically modeling the decisions of justices and how they relate to text.We incorporate into such a model texts authored by amici curiae (``friends of the court'' separate from the litigants) who seek to weigh in on the decision, then explicitly model their goals in a random utility model.We demonstrate the benefits of this approach in improved vote prediction and the ability to perform counterfactual analysis.

Authoring a scientific paper is a complex process involving many decisions.We introduce a probabilistic model of some of the important aspects of that process: that authors have individual preferences, that writing a paper requires trading off among the preferences of authors as well as extrinsic rewards in the form of community response to their papers, that preferences (of individuals and the community) and tradeoffs vary over time.Variants of our model lead to improved predictive accuracy of citations given texts and texts given authors.Further, our model's posterior suggests an interesting relationship between seniority and author choices.

Online debate forums are important social media for people to voice their opinions and debate with each other. Mining user stances or viewpoints from these forums has been a popular research topic. However, most current work does not address an important problem: for a specific issue, there may not be many users participating and expressing their opinions. Despite the sparsity of user stances, users may provide rich side information; for example, users may write arguments to back up their stances, interact with each other, and provide biographical information. In this work, we propose an integrated model to leverage side information. Our proposed method is a regression-based latent factor model which jointly models user arguments, interactions, and attributes. Our method can perform stance prediction for both warm-start and cold-start users. We demonstrate in experiments that our method has promising results on both micro-level and macro-level stance prediction.

We explore Debatepedia, a communityauthored encyclopedia of sociopolitical debates, as evidence for inferring a lowdimensional, human-interpretable representation in the domain of issues and positions.We introduce a generative model positing latent topics and cross-cutting positions that gives special treatment to person mentions and opinion words.We evaluate the resulting representation's usefulness in attaching opinionated documents to arguments and its consistency with human judgments about positions.0.1 0.2 0.3 0.4 0.5 comment, minimum, wage, poverty, capitalism nuclear, weapons, iran, states, threat party, vote, republican, political, voters energy, gas, power, fuel, wind

We seek to measure political candidates' ideological positioning from their speeches.To accomplish this, we infer ideological cues from a corpus of political writings annotated with known ideologies.We then represent the speeches of U.S. Presidential candidates as sequences of cues and lags (filler distinguished only by its length in words).We apply a domain-informed Bayesian HMM to infer the proportions of ideologies each candidate uses in each campaign.The results are validated against a set of preregistered, domain expertauthored hypotheses.