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Tweet Acts: How Constituents Lobby Congress via Twitter
Title
Tweet Acts: How Constituents Lobby Congress via Twitter
Creator
Hemphill, Libby, Roback, Andrew
Date
2014, 2014
Description
Twitter is increasingly becoming a medium through which constituents can lobby their elected representatives in Congress about issues that...
Show moreTwitter is increasingly becoming a medium through which constituents can lobby their elected representatives in Congress about issues that matter to them. Past research has focused on how citizens communicate with each other or how members of Congress (MOCs) use social media in general; our research examines how citizens communicate with MOCs. We contribute to existing literature through the careful examination of hundreds of citizen-authored tweets and the development of a categorization scheme to describe common strategies of lobbying on Twitter. Our findings show that contrary to past research that assumed citizens used Twitter to merely shout out their opinions on issues, citizens utilize a variety of sophisticated techniques to impact political outcomes.
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Data from Tweet Acts: How Constituents Lobby Congress via Twitter
Title
Data from Tweet Acts: How Constituents Lobby Congress via Twitter
Creator
Hemphill, Libby, Roback, Andrew
Date
2013-09-19, 2012
Description
Sponsorship: Amazon Web Services Education Grants Program
Data presented in a CSCW 2014 paper titled Tweet Acts: How Constituents Lobby...
Show moreSponsorship: Amazon Web Services Education Grants Program
Data presented in a CSCW 2014 paper titled Tweet Acts: How Constituents Lobby Congress via Twitter. Libby Hemphill and Andrew J. Roback. 2014. Tweet acts: how constituents lobby congress via Twitter. In Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing (CSCW '14). ACM, New York, NY, USA, 1200-1210. DOI=10.1145/2531602.2531735http://doi.acm.org/10.1145/2531602.2531735
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Relationships Among Twitter Conversation Networks, Language Use, and Congressional Voting
Title
Relationships Among Twitter Conversation Networks, Language Use, and Congressional Voting
Creator
Hemphill, Libby, Otterbacher, Jahna, Shapiro, Matthew A.
Date
2012-12-20, 2012
Description
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.
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Data used to develop #Polar scores
Title
Data used to develop #Polar scores
Creator
Culotta, Aron, Hemphill, Libby, Heston, Matthew
Date
2013, 2016
Description
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
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