Sharing the News in a Polarized Congress

This report describes findings from an analysis of all Facebook posts issued by members of the 114th and 115th Congresses between Jan. 2, 2015, and July 20, 2017. Researchers collected Facebook posts from members' officialFacebookaccounts using the social media platform's application programming interface (API) for public pages

Concept-Guided Chain-of-Thought Prompting for Pairwise Comparison Scaling of Texts with Large Language Models

Existing text scaling methods often require a large corpus, struggle with short texts, or require labeled data. We develop a text scaling method that leverages the pattern recognition capabilities of generative large language models (LLMs). Specifically, we propose concept-guided chain-of-thought (CGCoT), which uses prompts designed to summarize ideas and identify target parties in texts to generate concept-specific breakdowns, in many ways similar to guidance for human coder content analysis. CGCoT effectively shifts pairwise text comparisons from a reasoning problem to a pattern recognition problem. We then pairwise compare concept-specific breakdowns using an LLM. We use the results of these pairwise comparisons to estimate a scale using the Bradley-Terry model. We use this approach to scale affective speech on Twitter. Our measures correlate more strongly with human judgments than alternative approaches like Wordfish. Besides a small set of pilot data to develop the CGCoT prompts, our measures require no additional labeled data and produce binary predictions comparable to a RoBERTa-Large model fine-tuned on thousands of human-labeled tweets. We demonstrate how combining substantive knowledge with LLMs can create state-of-the-art measures of abstract concepts.Comment: 26 pages, 2 figure

A randomized phase 2 trial of gemcitabine/cisplatin with or without cetuximab in patients with advanced urothelial carcinoma

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/108368/1/cncr28767.pd

Bias in the Flesh

Replication data for studies 1-3 in "Bias in the Flesh Skin Complexion and Stereotype Consistency in Political Campaigns," see http://poq.oxfordjournals.org/content/early/2015/12/17/poq.nfv046.abstract

News40 simulation data

News40 simulation dat

Replication of Bias in the Flesh

Reanalysis data for replication of Bias in the Flesh Study

Replication Data for Estimating Heterogeneous Treatment Effects and the Effects of Heterogeneous Treatments with Ensemble Methods

This provides the replication code and data for the paper "Estimating Heterogeneous Treatment Effects and the Effects of Heterogeneous Treatments with Ensemble Methods"

Replication Data for: Exposure to Ideologically Diverse News and Opinion on Facebook

Replication Data for: Exposure to Ideologically Diverse News and Opinion on Facebook. This archive includes: R analysis code and aggregate data for deriving the main results (e.g., Table S5, S6) Python code and dictionaries for training and testing the hard-soft news classifier Aggregate summary statistics of the distribution of ideological homophily in networks. Aggregate summary statistics of the distribution of ideological alignment for hard content shared by the top 500 most shared websites. See README.html for a complete description of each file

Replication Data for: Projecting confidence: How the probabilistic horserace confuses and demobilizes the public

Recent years have seen a dramatic change in horserace coverage of elections in the U.S.---shifting focus from late-breaking poll numbers to sophisticated meta-analytic forecasts that emphasize candidates' chance of victory. Could this shift in the political information environment affect election outcomes? We use experiments to show that forecasting increases certainty about an election's outcome, confuses many, and decreases turnout. Furthermore, we show that election forecasting has become prominent in the media, particularly in outlets with liberal audiences, and show that such coverage tends to more strongly affect the candidate who is ahead---raising questions about whether they contributed to Trump's victory over Clinton in 2016. We bring empirical evidence to this question, using ANES data to show that Democrats and Independents expressed unusual confidence in a decisive 2016 election outcome---and that the same measure of confidence is associated with lower reported turnout