Data used to develop #Polar scores Culotta, Aron Hemphill, Libby Heston, Matthew twitter polarization U.S. Congress We present a new approach to measuring political polarization, including a novel algorithm and open source Python code, which leverages Twitter content to produce measures of polarization for both users and hashtags. #Polar scores provide advantages over existing measures because they (1) can be calculated throughout the legislative cycle, (2) allow for easy differentiation between users with similar scores, (3) are chamber-agnostic, and (4) are a generic approach that can be applied beyond the U.S. Congress. #Polar scores leverage available information such as party labels, word frequency, and hashtags to create an accessible, straightforward algorithm for estimating polarity using text. (from the paper: Hemphill, L., Culotta, A., and Heston, M. (forthcoming) #Polar Scores: Measuring partisanship using social media content. Journal of Information Technology & Politics.) The dataset contains one plain text TSV file with the following information for each of the 55,244 tweets used to develop #Polar scores : tweet_id, created_at, user_id, screen_name, tag, shortid, sex, party, state, chamber, name. The file contains one row per hashtag, and therefore tweets may appear more than once. The Python code for calculating #Polar scores is available here: http://doi.org/10.5281/zenodo.53888 2013 2016 Project deliverable other application/vnd.ms-excel islandora:1000013 http://hdl.handle.net/10560/3811 CS / Computer Science Illinois Institute of Technology Attribution 3.0 United States http://creativecommons.org/licenses/by/3.0/us/ In Copyright http://rightsstatements.org/page/InC/1.0/ Open Access