More Specificity, More Attention to Social Context: Reframing How We Address "Bad Actors"

To address "bad actors" online, I argue for more specific definitions of acceptable and unacceptable behaviors and explicit attention to the social structures in which behaviors occur.Comment: Paper submitted Workshop Paper Submitted to CHI 2018: Understanding "Bad Actors" Onlin

Learning the Lingo? Gender, Prestige and Linguistic Adaptation in Review Communities

Women 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

Challenges in Modifying Existing Scales for Detecting Harassment in Individual Tweets

In an effort to create new sociotechnical tools to combat online harassment, we developed a scale to detect and measure verbal violence within individual tweets. Unfortunately, we found that the scale, based on scales effective at detecting harassment offline, was unreliable for tweets. Here, we begin with information about the development and validation of our scale, then discuss the scale’s shortcomings for detecting harassment in tweets, and explore what we can learn from this scale’s failures. We explore how rarity, context, and individual coder’s differences create challenges for detecting verbal violence in individual tweets. We also examine differences in on- and offline harassment that limit the utility of existing harassment measures for online contexts. We close with a discussion of potential avenues for future work in automated harassment detection

“I’d have to vote against you”: Issue Campaigning via Twitter

Using 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

Tweet Acts: How Constituents Lobby Congress via Twitter

Twitter 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

Two Computational Models for Analyzing Political Attention in Social Media

Understanding how political attention is divided and over what subjects is crucial for research on areas such as agenda setting, framing, and political rhetoric. However, existing methods for measuring attention, such as manual labeling ac- cording to established codebooks, are expensive and restric- tive. We describe two computational models that automati- cally distinguish topics in politicians’ social media content. Our models - one supervised classifier and one unsupervised topic model - provide different benefits. The supervised clas- sifier reduces the labor required to classify content accord- ing to pre-determined topic lists. However, tweets do more than communicate policy positions. Our unsupervised model uncovers both political topics and other Twitter uses (e.g., constituent service). Together, these models are effective, in- expensive computational tools for political communication and social media research. We demonstrate their utility and discuss the different analyses they afford by applying both models to the tweets posted by members of the 115th U.S. Congress.This material is based upon work supported by the National Science Foundation under Grant No. 1822228.https://deepblue.lib.umich.edu/bitstream/2027.42/147460/6/Hemphill and Schopke - Two Compuational Models.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147460/1/Hemphill and Schopke - Two Computational Models.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147460/8/ICWSM 2020 Two Computational Models.pptx5056Description of Hemphill and Schopke - Two Compuational Models.pdf : Revised articleDescription of Hemphill and Schopke - Two Computational Models.pdf : Main articleDescription of ICWSM 2020 Two Computational Models.pptx : Presentation with scrip

More Specificity, More Attention to Social Context: Reframing How We Address ``Bad Actors''

Paper submitted to CHI 2018 Workshop: Understanding "Bad Actors" OnlineTo address ``bad actors'' online, I argue for more specific definitions of acceptable and unacceptable behaviors and explicit attention to the social structures in which behaviors occur.https://deepblue.lib.umich.edu/bitstream/2027.42/142392/1/bad_actors.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/142392/5/specificity-attention-social.pdfDescription of bad_actors.pdf : Previous versionDescription of specificity-attention-social.pdf : Main article - update

The Rhetorical Agenda: What Twitter Tells Us About Congressional Attention

Understanding how Members of Congress (MCs) distribute their political attention is key to a number of areas of political science research including agenda setting, framing, and issue evolution. Tweets illuminate what lawmakers are paying attention to by aggregating information from newsletters, press releases, and floor debates to provide a birds-eye view of a lawmaker’s diverse agenda. In order to leverage this data efficiently, we trained a supervised machine learning classifier to label tweets according to the Comparative Agenda Project’s Policy Codebook and used the results to examine the differential attention that policy topics receive from MCs. The classifier achieved an F1 score of 0.79 and a Cohen’s kappa with human labelers of 0.78, suggesting good performance. Using this classifier, we labeled 1,485,834 original MC tweets (Retweets were excluded) and conducted a multinomial logistic regression to understand what influenced the policy areas MCs Tweeted about. Our model reveals differences in political attention along party, chamber, and gender lines and their interactions. Our approach allows us to study MCs’ political attention in near real-time and to uncover both intra- and inter-group differences.https://deepblue.lib.umich.edu/bitstream/2027.42/148323/1/Rhetorical Agenda for MPSA 2019.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/148323/5/Political Attention under review.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/148323/6/Political Attention Supplementary Docs.pdf5056Description of Rhetorical Agenda for MPSA 2019.pdf : Paper presented at MPSA 2019Description of Political Attention under review.pdf : Manuscript submitted for peer reviewDescription of Political Attention Supplementary Docs.pdf : Peer review copy supplementary doc

Crafting Moral Infrastructures: How Nonprofits Use Facebook to Survive

We present findings from interviews with 23 individuals affiliated with non-profit organizations (NPOs) to understand how they deploy information and communication technologies (ICTs) in civic engagement efforts. Existing research about NPO ICT use is largely critical, but we did not find evidence that NPOs fail to use tools effectively. Rather, we detail how various ICT use on the part of NPOs intersects with unique affordance perceptions and adoption causes. Overall, we find that existing theories about technology choice (e.g., task-technology fit, uses and gratifications) do not explain the assemblages NPOs describe. We argue that NPOs fashion infrastructures in accordance with their moral economy frameworks rather than selecting tools based on utility. Together, the rhetorics of infrastructure and moral economies capture the motivations and constraints our participants expressed and challenge how prevailing theories of ICT usage describe the non-profit landscape.This work was supported in part by the National Science Foundation under Grant No. 1822228.https://deepblue.lib.umich.edu/bitstream/2027.42/145477/1/Hemphill-Million-Erickson-Crafting-moral-infrastructures.pdf56Description of Hemphill-Million-Erickson-Crafting-moral-infrastructures.pdf : Main articl