As Twitter becomes a more common means for officials to communicate with their constituents, it becomes more important that we understand just... Show moreAs Twitter becomes a more common means for officials to communicate with their constituents, it becomes more important that we understand just how that communication relates to other political activities. Using data from 411 members of Congress' Twitter activity during the summer of 2011, we examine relationships among the resulting conversation networks, language use, and political behavior. The social networks that result from their communications have surprisingly low density and high diameter, indicating a level of independence that is surprising for a group so tightly connected offline. Our findings also indicate that officials frequently use Twitter to advertise their political positions and to provide information but rarely to request political action from their constituents or to recognize the good work of others. Our analysis suggests strong relationships between anti-social behaviors indicated by the loosely connected network and low incidence of pro-social conversations and polarized or extreme Congressional voting records. Show less
We present a new approach to measuring political polarization, including a novel algorithm and open source Python code, which leverages... Show moreWe 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 Show less
