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Title: “I’d have to vote against you”: Issue Campaigning via Twitter
Creator: Roback, Andrew, Hemphill, Libby
Date: 2012-12-03, 2013
Description: Using tweets posted with #SOPA and #PIPA hashtags and directed at members of Congress, we identify six strategies constituents employ when...
Show moreUsing tweets posted with #SOPA and #PIPA hashtags and directed at members of Congress, we identify six strategies constituents employ when using Twitter to lobby their elected officials. In contrast to earlier research, we found that constituents do use Twitter to try to engage their officials and not just as a “soapbox” to express their opinions.
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Title: Asian American Chicago Network: A Case Study of Facebook Group Use By Immigrant Groups
Creator: Rao, Xi, Hemphill, Libby
Date: 2016, 2016
Publisher: ACM
Description: Through analyzing data from posts and about users, we describe how one particular Facebook group helps immigrants to the U.S. use social media...
Show moreThrough analyzing data from posts and about users, we describe how one particular Facebook group helps immigrants to the U.S. use social media to build a local community. As a preliminary study in intercultural communication through social media, we analyze one case, the Asian American Chicago Network (AACN) Facebook group, and uncover common topics users discuss and relationships between user tenure and various indicators of leadership and interaction. Our small finalized results from this preliminary project suggest that members of AACN likely use it (1) to build a professional network in the U.S.A., and (2) to reinforce and affirm their Asian culture and identities.
Sponsorship: National Science Foundation Award Number 1525662
Rao, X., & Hemphill, L. (2016). Asian American Chicago Network: A Case Study of Facebook Group Use By Immigrant Groups. In Proceedings of the 19th ACM Conference on Computer Supported Cooperative Work and Social Computing Companion (pp. 381–384). New York, NY, USA: ACM. http://doi.org/10.1145/2818052.2869077
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Title: Descriptors and measurements of verbal violence in tweets: toxicity README
Creator: Guberman, Joshua, Hemphill, Libby
Date: 2016, 2016
Description: Sponsorship: National Science Foundation Award # 1525662
The purpose of this data collection was to test a scale for detecting verbal violence...
Show moreSponsorship: National Science Foundation Award # 1525662
The purpose of this data collection was to test a scale for detecting verbal violence in Tweets. Workers at Mechanical Turk were first asked to complete a qualification test and then invited to code additional Tweets according to our scale. The qualification test involved a detailed explanation of each item of the scale, a walkthrough of a tweet that we had coded according to all 14 scale-items, a practice exercise, and a test. In the practice exercise, potential coders attempted to code a tweet on their own using our scale. After submitting their ratings, they were shown our own ratings for the same tweet and explanations for each of our ratings. The test component consisted of another coding task, in which coders were asked to code another tweet that we had already coded ourselves. The workers who, on test, with our ratings of that tweet on at least 11 out of the 14 items “passed” the test, earning the qualification that allowed them to participate in future coding tasks. Variables in the data include the ID of the Tweet (so that you may find it on Twitter; Twitter Terms of Service prohibit us from sharing the Tweets), the ID number we assigned to the coder, the rating that coder provided for each of the 14 items on our scale, the gender and age of the coder, and any comments the coder provided. APA (6th Edition) CITATION: Guberman, J. and Hemphill, L. (2016) Descriptors and measurements of verbal violence in tweets [data file and codebook]. doi: 10.6084/m9.figshare.3179368.
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Title: Descriptors and measurements of verbal violence in tweets
Creator: Guberman, Joshua, Hemphill, Libby
Date: 2016, 2016
Description: Sponsorship: National Science Foundation Award # 1525662
The purpose of this data collection was to test a scale for detecting verbal violence...
Show moreSponsorship: National Science Foundation Award # 1525662
The purpose of this data collection was to test a scale for detecting verbal violence in Tweets. Workers at Mechanical Turk were first asked to complete a qualification test and then invited to code additional Tweets according to our scale. The qualification test involved a detailed explanation of each item of the scale, a walkthrough of a tweet that we had coded according to all 14 scale-items, a practice exercise, and a test. In the practice exercise, potential coders attempted to code a tweet on their own using our scale. After submitting their ratings, they were shown our own ratings for the same tweet and explanations for each of our ratings. The test component consisted of another coding task, in which coders were asked to code another tweet that we had already coded ourselves. The workers who, on test, with our ratings of that tweet on at least 11 out of the 14 items “passed” the test, earning the qualification that allowed them to participate in future coding tasks. Variables in the data include the ID of the Tweet (so that you may find it on Twitter; Twitter Terms of Service prohibit us from sharing the Tweets), the ID number we assigned to the coder, the rating that coder provided for each of the 14 items on our scale, the gender and age of the coder, and any comments the coder provided. APA (6th Edition) CITATION: Guberman, J. and Hemphill, L. (2016) Descriptors and measurements of verbal violence in tweets [data file and codebook]. doi: 10.6084/m9.figshare.3179368.
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Title: Descriptors and measurements of verbal violence in tweets: toxicity_first_steps
Creator: Guberman, Joshua, Hemphill, Libby
Date: 2016, 2016
Description: Sponsorship: National Science Foundation Award # 1525662
The purpose of this data collection was to test a scale for detecting verbal violence...
Show moreSponsorship: National Science Foundation Award # 1525662
The purpose of this data collection was to test a scale for detecting verbal violence in Tweets. Workers at Mechanical Turk were first asked to complete a qualification test and then invited to code additional Tweets according to our scale. The qualification test involved a detailed explanation of each item of the scale, a walkthrough of a tweet that we had coded according to all 14 scale-items, a practice exercise, and a test. In the practice exercise, potential coders attempted to code a tweet on their own using our scale. After submitting their ratings, they were shown our own ratings for the same tweet and explanations for each of our ratings. The test component consisted of another coding task, in which coders were asked to code another tweet that we had already coded ourselves. The workers who, on test, with our ratings of that tweet on at least 11 out of the 14 items “passed” the test, earning the qualification that allowed them to participate in future coding tasks. Variables in the data include the ID of the Tweet (so that you may find it on Twitter; Twitter Terms of Service prohibit us from sharing the Tweets), the ID number we assigned to the coder, the rating that coder provided for each of the 14 items on our scale, the gender and age of the coder, and any comments the coder provided. APA (6th Edition) CITATION: Guberman, J. and Hemphill, L. (2016) Descriptors and measurements of verbal violence in tweets [data file and codebook]. doi: 10.6084/m9.figshare.3179368.
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Title: Tweeting Vertically? Elected Officials’ Interactions with Citizens on Twitter
Creator: Otterbacher, Jahna, Shapiro, Matthew A., Hemphill, Libby
Date: 2012-12-05, 2012
Description: Enthusiasts propose that social media promotes vertical political communication, giving citizens the opportunity to interact directly with...
Show moreEnthusiasts propose that social media promotes vertical political communication, giving citizens the opportunity to interact directly with their representatives. However, skeptics claim that politicians avoid direct engagement with constituents, using technology to present a façade of interactivity instead. This study explores if and how elected officials in three regions of the world are using Twitter to interact with the public. We examine the Twitter activity of 15 officials over a period of six months. We show that in addition to the structural features of Twitter that are designed to promote interaction, officials rely on language to foster or to avoid engagement. It also provides yet more evidence that the existence of interactive features does not guarantee interactivity.
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Title: Doing What I Say: Connecting Congressional Social Media Behavior and Congressional Voting
Creator: Shapiro, Matthew A., Hemphill, Libby, Otterbacher, Jahna
Date: 2012-03-10, 2012-03-10
Description: Public officials’ communication has been explored at length in terms of how such their statements are conveyed in the traditional media, but...
Show morePublic officials’ communication has been explored at length in terms of how such their statements are conveyed in the traditional media, but minimal research has been done to examine their communication via social media. This paper explores the kinds of statements U.S. officials are making on Twitter in terms of the actions they are trying to achieve. We then analyze the correlation between these statements, Congressional communication network structures, and voting behavior. Our analysis leverages over 29,000 tweets by members of Congress in conjunction with existing DW-NOMINATE voting behavior data. We find that pro-social and self-promoting statements correlate with Congressional voting records but that position within the Congressional communication network does not correlate with voting behavior.
Sponsorship: Social Networks Research Group at IIT, IIT Graduate College
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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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