Critical and Ethical Studies of Digital and Social Media

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    Uncoupling inequality: Reflections on the ethics of benchmarks for digital media
    ( 2022-01-04) Washington, Anne ; Rhue, Lauren A. ; Nakamura, Lisa ; Stevens, Robin
    Our collaboration seeks to demonstrate shared interrogation by exploring the ethics of machine learning benchmarks from a socio-technical management perspective with insight from public health and ethnic studies. Benchmarks, such as ImageNet, are annotated open data sets for training algorithms. The COVID-19 pandemic reinforced the practical need for ethical information infrastructures to analyze digital and social media, especially related to medicine and race. Social media analysis that obscures Black teen mental health and ignores anti-Asian hate fails as information infrastructure. Despite inadequately handling non-dominant voices, machine learning benchmarks are the basis for analysis in operational systems. Turning to the management literature, we interrogate cross-cutting problems of benchmarks through the lens of coupling, or mutual interdependence between people, technologies, and environments. Uncoupling inequality from machine learning benchmarks may require conceptualizing the social dependencies that build structural barriers to inclusion.
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    “Tinder Will Know You Are A 6”: Users’ Perceptions of Algorithms on Tinder
    ( 2022-01-04) Abel, Christie ; Pei, Lucy ; Larson, Ian ; Olgado, Benedict Salazar ; Turner, Benedict
    Through in-depth interviews of 22 Tinder users, we explore how users interpret their algorithmically mediated experience on the platform. We find that users have various explanations of whether and how Tinder uses algorithms and that users have varying degrees of certainty about these explanations. In response, users report that they act in particular ways given their explanations and degree of certainty. We discuss how users, as part of their sensemaking practice around how algorithms work, engage in forms of improvisation. In addition, we argue that algorithm awareness leads to a more nuanced acknowledgement of inequality and power, including the power-laden roles of platforms themselves.
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    “This Is the Future of Advertising!” Or Is It? New Insights into the Justifiability of Deceptive Crowdwork in Cyberspace
    ( 2022-01-04) Kauppila, Santtu ; Soliman, Wael
    Unlike classical forms of deception where the deceiver deceives their victims directly, the crowdsourcing of cyber deception provides a powerful and cost-effective mechanism for deceivers to create and spread falsehood from the shadows. But for a mass deception campaign to be effective, the crowdworkers must rationalize (and willingly accept) their role in the deceptive act. What, then, could justify participation in a mass-deception campaign? To answer this question, we adopt the qualitative vignette approach and utilize neutralization theory as our guiding lens. Our results point to several neutralization techniques that crowdworkers could invoke to convincingly rationalize involvement in a cyber deception campaign. Importantly, the findings shed new light on a growing pessimism about work ethics in cyberspace which may lead some ordinary people into joining deception campaigns, believing it to be the future of advertising. We discuss the theoretical and practical implications of these novel insights.
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    The Impact of the Covid-19 Cases with Twitter Users in Their Perception of the Brazilian Government
    ( 2022-01-04) Júnior, Celso ; Loutfi, Marcelo ; Wolfgand Matsui Siqueira, Sean
    During the COVID-19 pandemic, political discussions in Brazil revolved around the pandemic and the controversial leader Jair Bolsonaro. Twitter reflected these discussions, also bringing reports of user dramas that had family members victimized by Covid-19. This study investigates their perception of the federal government through a quantitative and qualitative analysis of these users’ tweets. We have identified 3,756 Twitter users who reported cases of family members with Covid-19 and collected their government-related tweets before and after those reports. We analyzed the feelings expressed in these tweets using automated techniques and extracted a sample of 650 tweets that had difficult-to-understand terms for a manual analysis of the feelings. The study found subtle changes in the perceptions of people who approve or disapprove of the federal government.
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    Targeted Ads and/as Racial Discrimination: Exploring Trends in New York City Ads for College Scholarships
    ( 2022-01-04) Chang, Ho-Chun Herbert ; Bui, Matthew ; Mcilwain, Charlton
    This paper uses and recycles data from a third-party digital marketing firm, to explore how targeted ads contribute to larger systems of racial discrimination. Focusing on a case study of targeted ads for educational searches in New York City, it discusses data visualizations and mappings of trends in the advertisements’ targeted populations alongside U.S census data corresponding to these target zipcodes. We summarize and reflect on the results to consider how internet platforms systemically and differentially target advertising messages to users based on race; the tangible harms and risks that result from an internet traffic system designed to discriminate; and finally, novel approaches and frameworks for further auditing systems amid opaque, black-boxed processes forestalling transparency and accountability.