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.


author = {Libby Hemphill and Aron Culotta and Matthew Heston},
title = {\#Polar Scores: Measuring Partisanship Using Social Media Content},
journal = {Journal of Information Technology \& Politics},
volume = {0},
number = {ja},
pages = {null},
year = {2016},
doi = {10.1080/19331681.2016.1214093},