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Title: Hashtag data from "Agenda Building & Indexing: Does the U.S. Congress Direct New York Times Content through Twitter?"
Creator: Hemphill, Libby
Date: 2016, 2014
Description: From the paper: The conventional understanding of how elected officials affect the policy agenda is based in arguments that they use symbols...
Show moreFrom the paper: The conventional understanding of how elected officials affect the policy agenda is based in arguments that they use symbols and rhetoric to propagate the problem, and that this happens primarily through the traditional media. The arguments presented in this article are largely consistent with this but account for the function of social media. More specifically, and framed by indexing theory, we argue that social media enhances opportunities for policy agenda builders in the U.S. Congress to share information with journalists. Across the key policy issues of 2013, tests for congruence between politicians’ Twitter posts and New York Times articles confirm a connection, particularly for the policy issue areas of the economy, immigration, health care, and marginalized groups. Simultaneous discussion and debate between Democrats and Republicans about a particular policy issue area, however, negatively impacts how the New York Times indexes a particular issue.
Here we provide single Excel file of all the hashtags posted by members of Congress to Twitter during 2013. The file contains three columns: datetime, hashtag, and twitter_username. The datetime indicates when a tweet was posted. The hashtag indicates what hashtag a user posted at that time (tweets may contain multiple tags). Twitter_username is the Twitter handle of the account that posted a tweet with that hashtag at that time. We created a list of member of Congress Twitter accounts by looking up each member and checking with Govtrack.us and congress.gov information. Please cite our paper: Shapiro, M. A. and Hemphill, L. (in press) Agenda Building & Indexing: Does the U.S. Congress Direct New York Times Content through Twitter? Policy & Internet.
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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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Title: Building science measurements in the Hospital Microbiome Project
Creator: Stephens, Brent
Date: 2014, 2014
Description: Sponsorship: Alfred P. Sloan Foundation’s program on the Microbiology of the Built Environment (Grant No. 2012-10-04)

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: Analysis on the Persisting Effects of Redlining on Green Infrastructure in Chicago Neighborhoods - Neighborhood Map files
Creator: Meyer, Joel, Eshraghi, Amirreza
Date: 2023
Description: This compressed collection of map files constitutes data used in "Analysis on the Persisting Effects of Redlining on Green Infrastructure in...
Show moreThis compressed collection of map files constitutes data used in "Analysis on the Persisting Effects of Redlining on Green Infrastructure in Chicago Neighborhoods", published in SoReMo, March 2023 (DOI: https://doi.org/10.18409/soremojournal.v3i1.221). The report looks at “redlining” maps produced by the Home Owners’ Loan Corporation (HOLC) to analyze the current green infrastructure levels in various neighborhoods of Chicago.
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Title: Views on Ethical Issues in Research Labs: a University-Wide Survey
Creator: Laas, Kelly, Taylor, Stephanie, Miller, Christine Z, Brey, Eric M, Hildt, Elisabeth
Date: 2021
Description: Full survey used for "Views on Ethical Issues in Research Labs" published in the journal Accountability in Research: Policies and Quality...
Show moreFull survey used for "Views on Ethical Issues in Research Labs" published in the journal Accountability in Research: Policies and Quality Assurance in 2021. This survey was completed in 2017.
Sponsorship: National Science Foundation
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Collection: Accountability in Research: Policies and Quality Assurance

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