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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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Title: Chujio (Semester Unknown) IPRO 303: ChujioIPRO302FinalReportF10
Creator: Kumar, Aditi, Curtis, Christopher, Vysotskiy, Dmitriy, Ramirez, Ernesto, Abu-amara, Hashem, Chun, Jason, Jewell, John, Varga, Kalman, Michael, Mark, Koto, Melanie, Tagny, Patrick
Date: 2010, 2010-12
Description: Solutions through Coaliition
Sponsorship: NAVTEQ
Deliverables

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: Chujio (Semester Unknown) IPRO 303: ChujioIPRO302BrochureF10
Creator: Kumar, Aditi, Curtis, Christopher, Vysotskiy, Dmitriy, Ramirez, Ernesto, Abu-amara, Hashem, Chun, Jason, Jewell, John, Varga, Kalman, Michael, Mark, Koto, Melanie, Tagny, Patrick
Date: 2010, 2010-12
Description: Solutions through Coaliition
Sponsorship: NAVTEQ
Deliverables

Title: Chujio (Semester Unknown) IPRO 303: ChujioIPRO302Poster1F10
Creator: Kumar, Aditi, Curtis, Christopher, Vysotskiy, Dmitriy, Ramirez, Ernesto, Abu-amara, Hashem, Chun, Jason, Jewell, John, Varga, Kalman, Michael, Mark, Koto, Melanie, Tagny, Patrick
Date: 2010, 2010-12
Description: Solutions through Coaliition
Sponsorship: NAVTEQ
Deliverables

Title: Chujio (Semester Unknown) IPRO 303
Creator: Kumar, Aditi, Curtis, Christopher, Vysotskiy, Dmitriy, Ramirez, Ernesto, Abu-amara, Hashem, Chun, Jason, Jewell, John, Varga, Kalman, Michael, Mark, Koto, Melanie, Tagny, Patrick
Date: 2010, 2010-12
Description: Solutions through Coaliition
Sponsorship: NAVTEQ
Deliverables

Title: Chujio (Semester Unknown) IPRO 303: ChujioIPRO302Poster2F10
Creator: Kumar, Aditi, Curtis, Christopher, Vysotskiy, Dmitriy, Ramirez, Ernesto, Abu-amara, Hashem, Chun, Jason, Jewell, John, Varga, Kalman, Michael, Mark, Koto, Melanie, Tagny, Patrick
Date: 2010, 2010-12
Description: Solutions through Coaliition
Sponsorship: NAVTEQ
Deliverables

Title: Chujio (Semester Unknown) IPRO 303: ChujioIPRO302MidTermF10
Creator: Kumar, Aditi, Curtis, Christopher, Vysotskiy, Dmitriy, Ramirez, Ernesto, Abu-amara, Hashem, Chun, Jason, Jewell, John, Varga, Kalman, Michael, Mark, Koto, Melanie, Tagny, Patrick
Date: 2010, 2010-12
Description: Solutions through Coaliition
Sponsorship: NAVTEQ
Deliverables

Title: Chujio (Semester Unknown) IPRO 303: ChujioIPRO302ProjectPlanF10_redacted
Creator: Kumar, Aditi, Curtis, Christopher, Vysotskiy, Dmitriy, Ramirez, Ernesto, Abu-amara, Hashem, Chun, Jason, Jewell, John, Varga, Kalman, Michael, Mark, Koto, Melanie, Tagny, Patrick
Date: 2010, 2010-12
Description: Solutions through Coaliition
Sponsorship: NAVTEQ
Deliverables

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 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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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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