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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: Learning the Lingo? Gender, Prestige and Linguistic Adaptation in Review Communities
Creator: Hemphill, Libby, Otterbacher, Jahna
Date: 2011-11-19, 2012-02
Publisher: ACM Press
Description: Women and men communicate differently in both face-to- face and computer-mediated environments. We study linguistic patterns considered...
Show moreWomen and men communicate differently in both face-to- face and computer-mediated environments. We study linguistic patterns considered gendered in reviews contributed to the Internet Movie Database. IMDb has been described as a male-majority community, in which females contribute fewer reviews and enjoy less prestige than males. Analyzing reviews posted by prolific males and females, we hypothesize that females adjust their communication styles to be in sync with their male counterparts. We find evidence that while certain characteristics of “female language” persevere over time (e.g., frequent use of pronouns) others (e.g., hedging) decrease with time. Surprisingly, we also find that males often increase their use of “female” features. Our results indicate, that even when they resemble men’s reviews linguistically, women’s reviews still enjoy less prestige and smaller audiences.
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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: What's Congress Doing on Twitter?
Creator: Hemphill, Libby, Otterbacher, Jahna, Shapiro, Matthew A.
Date: 2013-10-23, 2012-10-23
Description: As Twitter becomes a more common means for officials to communicate with their constituents, it becomes more important that we understand how...
Show moreAs Twitter becomes a more common means for officials to communicate with their constituents, it becomes more important that we understand how officials use these communication tools. Using data from 380 members of Congress’ Twitter activity during the winter of 2012, we find 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. We highlight a number of differences in communication frequency between men and women, Senators and Representatives, Republicans and Democrats. We provide groundwork for future research examining the behavior of public officials online and testing the predictive power of officials’ social media behavior.
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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: 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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