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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: Looking for (Lesbian) Love: Social Media Subtext Readings of Rizzoli and Isles
Creator: Hemphill, Libby
Date: 2015, 2015
Description: Paper presented at #IR16 the annual conference of the Association of Internet Researchers
Paper presented at #IR16 the annual conference of the...
Show morePaper presented at #IR16 the annual conference of the Association of Internet Researchers
Paper presented at #IR16 the annual conference of the Association of Internet Researchers
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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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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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