Social Identity | Digital Divide Dynamics | Techno-Mediated Justice: UMSI Research Roundup
Monday, 06/09/2025
By Noor HindiUniversity of Michigan School of Information faculty and PhD students are creating and sharing knowledge that helps build a better world. Here are some of their recent publications.
Publications
Different norms of sexual activity and consent seeking among college students: Social identity and statistical discrimination
Journal of Economic Behavior and Organization, July 2025
Preventing sexual assault on university campuses is rooted in promoting the adoption and practice of seeking consent. Using identity theory and a factorial vignette survey experiment, we test for the presence of implicit differences in appropriateness ratings based on context, gender, race and sexual orientation and, in aggregate, differences in social norms that govern college students’ sexual interactions. We provide a simple theoretical framework of statistical discrimination where the social norms for identical actions are predicted to differ because the appropriateness of actions is imperfectly observed and evaluators hold beliefs about underlying propensities of appropriate action that are rooted in identity. Our results show that context significantly alters perceptions of appropriate behavior and that heterosexual male actions are viewed as systematically less socially appropriate. We validate our findings with a post-study questionnaire which reveals that beliefs regarding appropriateness ratings are largely driven by the perceived rates of sexual assault among the represented population by the vignette narrator. The paper advances the study of norms rooted in identity and presents an identity-based theoretical framework that provides intuition for how such a difference may arise.
Quantifying Narrative Similarity Across Languages
Sociological Methods and Research, June 2025
Hannah Waight∗, Solomon Messing∗, Anton Shirikov, Margaret E. Roberts, Jonathan Nagler, Jason Greenfield, Megan A. Brown, Kevin Aslett, Joshua A. Tucker
How can one understand the spread of ideas across text data? This is a key measurement problem in sociological inquiry, from the study of how interest groups shape media discourse, to the spread of policy across institutions, to the diffusion of organizational structures and institution themselves. To study how ideas and narratives diffuse across text, we must first develop a method to identify whether texts share the same information and narratives, rather than the same broad themes or exact features. We propose a novel approach to measure this quantity of interest, which we call “narrative similarity,” by using large language models to distill texts to their core ideas and then compare the similarity of claims rather than of words, phrases, or sentences. The result is an estimand much closer to narrative similarity than what is possible with past relevant alternatives, including exact text reuse, which returns lexically similar documents; topic modeling, which returns topically similar documents; or an array of alternative approaches. We devise an approach to providing out-of-sample measures of performance (precision, recall, F1) and show that our approach outperforms relevant alternatives by a large margin. We apply our approach to an important case study: The spread of Russian claims about the development of a Ukrainian bioweapons program in U.S. mainstream and fringe news websites. While we focus on news in this application, our approach can be applied more broadly to the study of propaganda, misinformation, diffusion of policy and cultural objects, among other topics.
Candidates Be Posting: Multi-Platform Strategies and Partisan Preferences in the 2022 U.S. Midterm Elections
Social Media and Society, May 2025
Josephine Lukito, Maggie Macdonald, Bin Chen, Megan A. Brown, Stephen Prochaska, Yunkang Yang, Jason Greenfield, Jiyoun Suk, Wei Zhong, Ross Dahlke, Porismita Borah
In this multi-platform, comparative study, we analyze social media messages from political candidates (N = 1,517) running for Congress during the 2022 U.S. Midterm election. We collect data from seven social media platforms: Facebook, Twitter, Truth Social, Gettr, Instagram, YouTube, and Rumble over the 4 weeks before and after election day. With this unique dataset of posts, we apply computational methods to identify messages that sought to mobilize individuals (online and offline) to donate money, vote, attend events, engage with the campaign online, and visit the campaign’s content on other platforms. We find that Democrats were not on alt-tech platforms in 2022 and that both Republicans and Democrats use video-based platforms for multiple mobilization strategies. Mobilization messages varied for House and Senate candidates of both parties across platforms, before and after election day.
“Getting people access to services is also getting them access to a phone”: Clarifying digital divide dynamics and their consequences in Community Mental Health Care
AMIA Annual Symposium Proceedings Archive, May 2025
Alicia K Williamson, Ella Jiaqi Li, Tiffany C Veinot
Access to mental healthcare is increasingly technologically-mediated. People with low socioeconomic status (SES) and serious mental illness (SMI) face lower rates of tech ownership and may lack technological skills, called “digital divides.” Yet, little is known about how digital divides may impact mental healthcare access. Therefore, a qualitative study (ethnographic observations and interviews) was conducted with stakeholders working with low-SES SMI patients using community mental health care (CMH) (N=14). Findings showed that consumers struggled to maintain consistent internet—and thus mental healthcare—access despite owning smartphones. Consumers frequently faced care disruptions due to broken, lost, or uncharged phones. Staff and patients created effortful but ad-hoc workarounds to restore access during technological access disruptions. These solutions frequently occurred after healthcare appointments were missed. Digital divide concepts should accommodate the work necessary to maintain technology access even after ownership and its impact on care access—especially among low-SES SMI patients.
Restricting the Link: Effects of Focused Attention and Time Delay on Phishing Warning Effectiveness
2025 IEEE Symposium on Security and Privacy, May 2025
Justin Petelka, Benjamin Berens, Carlo Sugatan, Melanie Volkamer, Florian Schaub
Phishing warning researchers have proposed two forms of hyperlink restrictions for reducing phishing clickthrough rates: focused attention, which prevents users from proceeding to a suspicious URL until they click the uncovered link inside the warning; and time delay, which disables link clicking for a short period of time. Both measures aim to draw user attention to the warning and nudge them to carefully evaluate the respective link’s URL. However, the effectiveness of these measures has so far not been comparatively evaluated. We conducted a mixed-methods online experiment (n=1,320) to understand differences in the effectiveness of focused attention and time delay both independently and together. Our study used an instrumented email inbox environment, in which participants were asked to assess emails and email hyperlinks. We found that, while both focused attention and time delay reduced click-through rates independently, the strength of these effects were significantly different from each other with focused attention being more effective than time delay. Combining both measures reduced CTR even further. We also found that participants who saw a warning with a time delay were more likely to hover over hyperlinks for longer than those who saw a focused attention warning. We discuss the implications of our findings for the design of anti-phishing warnings.
The development of information infrastructure and technological capabilities used to manage social care and address quality in primary care settings
BMC Health Services Research, May 2025
Anthony M. Provenzano, Faiyaz Syed, Jodyn E. Platt, Gretchen A. Piatt, Mark S. Ackerman, Ayse Buyuktur, Michael S. Klinkman
Background: With new payment systems to prompt more sophisticated data activities, primary care practices are developing technological capabilities to manage patient care and information. One burgeoning capability is the collection of social determinants of health (SDOH) data and using that information to provide social care. This study describes the information infrastructure and technological capabilities developed by community health centers (CHCs) and examines the factors influencing SDOH data integration and management in primary care practice. It offers health care leaders insights and strategies to build capacity for managing social care and quality.
Methods: An observational design was used to examine the technological capabilities of CHCs in Michigan via a practice survey, and factors related to developing information infrastructure were qualitatively explored. The practice survey, semi-structured interviews, and national health center data were analyzed. Sociotechnical systems and organizational theories were used to develop the survey and interview guide. A sample of Michigan CHCs (n=15) was recruited for the study. The practice survey was administered to CHC leaders, clinicians, and staff (n=27). Semistructured interviews (n=25) were then conducted to explore infrastructural, organizational, and technological factors associated with managing social care and information.
Results: Michigan CHCs developed capabilities to exchange patient information with state and local partners. Data were typically shared with maternal and infant health (n=5, 33.3%), mental health (n=5, 33.3%), substance use (n=6, 40%), domestic violence (n=6, 40%), and food assistance (n=6, 40%) providers, but CHCs did not develop the same capabilities with all social services examined. The interviews revealed that CHCs leveraged health care and government investments in information technology (IT) as a strategy to share data and address quality. The survey results revealed that CHCs developed the ability to use SDOH data to manage population health and provide value-based care.
Techno-mediated Justice: How Restorative Justice Practitioners Use VideoConferencing Platforms and What HCI Can Learn from Them
Proceedings of the ACM on Human-Computer Interaction, May 2025
Tyler Musgrave, Alexis Bell, Sarita Schoenebeck
Since the beginning of the COVID-19 pandemic, videoconferencing platforms have become an essential part of our social, educational, and work lives. Furthermore, the United States continues to grapple with criminal justice reform, employing processes such as restorative justice to replace antiquated punitive approaches to justice with a focus on the multifaceted needs of communities. Essential to the delivery of restorative justice, restorative justice practitioners are community members trained in the process and approaches of restorative practice. Like many others, restorative justice practitioners have transitioned from historically in-person facilitation to online facilitation, integrating videoconferencing platforms into their justice-related work. Diverse communities within Human-Computer Interaction (HCI) have been examined in light of their digital transitions and the utilization of various digital tools, including videoconferencing platforms prompted by the pandemic. However, the distinctive perspective of restorative justice practitioners in adapting restorative approaches to an online format is unexplored. Therefore, our research examines how restorative practitioners use videoconferencing platforms for justice-related interventions. To do so, we conducted six semi-structured focus group interviews with 22 US-based restorative facilitators to learn about their experiences with online delivery of restorative justice. Our research revealed that restorative facilitators uphold restorative values in their online delivery by embracing the restorative facilitation process and fostering dialogue. Furthermore, we posit that the Human-Computer Interaction (HCI) community can acquire valuable insights from restorative practitioners on nurturing and sustaining intimacy and connection online.
The need for scientific leadership and collaboration to enhance social connection: A call to action
Annals of the New York Academy of Sciences, May 2025
Julianne Holt-Lunstad, Thomas K. M. Cudjoe, Dani Dumitriu, Nicole B. Ellison, Ashwin A. Kotwal, Matthew S. Pantell, Carla M. Perissinotto, Matthew Lee Smith
The United States faces a growing crisis of social disconnection, marked by increasing rates of loneliness, social isolation, and declining social capital. This has profound implications for public health, as social connection is critical to individual well-being and societal functioning. The “loneliness epidemic,” as described by the US Surgeon General, is intertwined with broader challenges such as mental health crises, substance abuse, and sociopolitical issues. Although evidence highlights the importance of social connection for health outcomes, efforts to address social disconnection remain fragmented. This article provides context about the status of social disconnection in America and justifies the need for science to promote social connection from the perspectives of a scientific leadership council (SLC). This call to action proposes coordinated efforts to: (1) galvanize efforts to employ scientific evidence to design solutions and policies to address social disconnection; (2) establish the role of a US-based SLC, an interdisciplinary collaborative for evidence-based leadership; and (3) advocate for unified efforts and harmonization to close the gap between evidence and implementation. Additionally, this article proposes setting measurable national goals aligned with the Healthy People 2030 framework to monitor progress and drive systemic change, transforming the current landscape and building a more connected future.
Sociocultural Factors in Digital Skills Learning: A Community-Based Intervention Among U.S. Public Housing Adults
Proceedings of the ACM on Human-Computer Interaction, May 2025
Soyoung Lee, Julie Hui, Tawanna R. Dillahunt
Digital skills are essential for engaging in employment, healthcare, education, and government services. However, the digital divide remains a social inequality, especially among marginalized populations. Through a community-engaged research approach, we conducted a digital skills learning intervention in a U.S. public housing community, where residents frequently face socioeconomic challenges and limited access to digital resources. Public housing is a community seldom explored in CSCW and HCI research and provides a unique context to study the ongoing digital skills gap. Through the lens of situated learning theory, we study how sociocultural factors impact the efficacy of a community-based computer skills learning intervention. Specifically, we examine how the public housing community organized various resources—online learning materials, instructors, peer social support, and on-the-job learning opportunities—for digital skills development. Notably, the training leveraged instructor critical care and peer support to develop a learning community between residents and leaders of the community NGO that continued beyond the formalized training program. We contribute to CSCW and HCI work on collective and assets-based approaches to enhancing digital capacity. Our work provides implications for building collective grassroots digital skills learning infrastructure that could create new digitally-engaged employment opportunities.
Disrupted Behavioral Health Routines for Children During a Public Health Crisis: A Mixed-Methods Study of Digital Technology’s Role in Routine Recovery
Proceedings of the ACM on Human-Computer Interaction, April 2025
Olivia K. Richards, Allison Spiller, Carol F. Scott, Tiffany Veinot
The COVID-19 pandemic caused a global disruption of daily routines. Children with behavioral disabilities were particularly impacted, losing access to critical face-to-face behavioral health services. In response, many providers and parents attempted to recover these routine services, primarily with digital technology. In April-July 2020, we conducted a mixed-methods study with parents of children with behavioral disabilities. Using a six-week survey study followed by semi-structured interviews, we identified how disrupted behavioral health routines impacted children, and how care teams recovered these services using digital technology. The recovery of children's behavioral health services was delayed, resulting in negative consequences for the children. The stoppage of services undermined care teams' coordination mechanisms, necessitating they establish digital communication channels. This digital communication supported the recovery of some services, but not most. Video conferencing was overstimulating for some children, and most recovered services required parental involvement. Our findings have substantial implications for the CSCW community regarding the design of digital technology to increase usability by (and supports for) children with disabilities, and how behavioral health practice could enable resilient behavioral health services that could withstand the impact of future disruption.
Going the Distance: Achieving Sufficiency in Data Reuse
Harvard Data Science Review, April 2025
Open science is a cornerstone of knowledge production. Scholars and government agencies have outlined multiple benefits for open science including increased transparency in scientific processes, the data, and results leading to greater trust in science by the populace; more robust science; the ability to test and replicate studies and thus verify results; and faster innovation and testing of new theories ; ; ; . Two pillars of open science are data producers sharing data in ways that allow reuse and data reusers’ ability to work with data they often did not produce. Still, data reuse is a perplexing problem. Not only is it a difficult challenge to solve, but it is also a difficult phenomenon to both encourage and track. Federal agencies have attempted to facilitate reuse indirectly by requiring data sharing ; . Yet, as assert in their article, “From Data Creator to Data Reuser: Distance Matters,” “Sharing research data is necessary, but not sufficient, for data reuse.”
Beyond Language Translation
Asian Americans Advancing Justice | AAJC, April 2025
Matt Bui, David Mori, U-M, Jenny Liu
This report explores how the digital media use and experiences of Asian Americans are shaped by various cultural, linguistic, and generational factors (e.g., age, ethnicity, education level, and political affiliation). It draws from the reported experiences of 101 youth and adults (aged 15-91) captured across 12 focus groups conducted across the United States.
Model predictive control in mHealth: a decision framework for optimised personalised physical activity interventions
International Journal of Control, April 2025
Mohamed El Mistiri, Daniel E. River, Pedrag Klasnja, Junghwan Park, Eric Heckler
A major problem in global health is insufficient physical activity (PA) by individuals, despite its proven benefits. In this paper, Model Predictive Control (MPC) is evaluated as the basis for delivering personalised optimal adaptive behavioural interventions aimed at improving PA (in terms of the number of steps walked per day). Utilising the behavioural framework of Social Cognitive Theory (SCT) expressed as a fluid analogy computational model, a series of diverse control strategies are proposed under different circumstances that provide insights into how MPC can serve as a broad-based framework for delivering PA behavioural interventions. The complexities of measurement and information availability, physical and budgetary constraints, and plant limitations and their impact on decision-making are explored, with the results obtained demonstrating MPC's potential to deliver feasible, personalised, and user-friendly behavioural interventions under conditions involving limited measurements, nonlinearity, and plant-model mismatch.
Causally Modeling the Linguistic and Social Factors that Predict Email Response
Association for Computational Linguistics, April 2025
Yinuo Xu*, Hong Chen*, Sushrita Rakshit*, Aparna Ananthasubramaniam*, Omkar Yadav*, Mingqian Zheng*, Michael Jiang*, Lechen Zhang*, Bowen Yi*, Kenan Alkiek*, Abraham Israeli*, Bangzhao Shu*, Hua Shen*, Jiaxin Pei*, Haotian Zhang*, Miriam Schirmer*, David Jurgens
Email is a vital conduit for human communication across businesses, organizations, and broader societal contexts. In this study, we aim to model the intents, expectations, and responsiveness in email exchanges. To this end, we release SIZZLER, a new dataset containing 1800 emails annotated with nuanced types of intents and expectations. We benchmark models ranging from feature-based logistic regression to zero-shot prompting of large language models. Leveraging the predictive model for intent, expectations, and 14 other features, we analyze 11.3M emails from GMANE to study how linguistic and social factors influence the conversational dynamics in email exchanges. Through our causal analysis, we find that the email response rates are influenced by social status, argumentation, and in certain limited contexts, the strength of social connection.
Pre-prints, Working Papers, Articles, Workshops and Talks
Post-Post-API Age: Studying Digital Platforms in Scant Data Access Times
arXiv, May 2025
Kayo Mimizuka, Megan A Brown, Kai-Cheng Yang, Josephine Lukito
Over the past decade, data provided by digital platforms has informed substantial research in HCI to understand online human interaction and communication. Following the closure of major social media APIs that previously provided free access to large-scale data (the “post-API age”), emerging data access programs required by the European Union’s Digital Services Act (DSA) have sparked optimism about increased platform transparency and renewed opportunities for comprehensive research on digital platforms, leading to the “post-post-API age.” However, it remains unclear whether platforms provide adequate data access in practice. To assess how platforms make data available under the DSA, we conducted a comprehensive survey followed by indepth interviews with 19 researchers to understand their experiences with data access in this new era. Our findings reveal significant challenges in accessing social media data, with researchers facing multiple barriers including complex API application processes, difficulties obtaining credentials, and limited API usability. These challenges have exacerbated existing institutional, regional, and financial inequities in data access. Based on these insights, we provide actionable recommendations for platforms, researchers, and policymakers to foster more equitable and effective data access, while encouraging broader dialogue within the CSCW community around interdisciplinary and multi-stakeholder solutions.
The Effects of Moral Framing on Online Fundraising Outcomes: Evidence from GoFundMe Campaigns
arXiv, May 2025
This study examines the impact of moral framing on fundraising outcomes, including both monetary and social support, by analyzing a dataset of 14,088 campaigns posted on GoFundMe. We focused on three moral frames: care, fairness, and (ingroup) loyalty, and measured their presence in campaign appeals. Our results show that campaigns in the Emergency category are most influenced by moral framing. Generally, negatively framing appeals by emphasizing harm and unfairness effectively attracts more donations and comments from supporters. However, this approach can have a downside, as it may lead to a decrease in the average donation amount per donor. Additionally, we found that loyalty framing was positively associated with receiving more donations and messages across all fundraising categories. This research extends existing literature on framing and communication strategies related to fundraising and their impact. We also propose practical implications for designing features of online fundraising platforms to better support both fundraisers and supporters.
Computing for Community-Based Economies: A Sociotechnical Ecosystem for Democratic, Egalitarian and Sustainable Futures
The SAO Astrophysics Data System, April 2025
Kwame Porter Robinson, Ron Eglash, Lionel Robert, Audrey Bennett, Mark Guzdial, Michael Nayebare
Automation and industrial mass production, particularly in sectors with low wages, have harmful consequences that contribute to widening wealth disparities, excessive pollution, and worsened working conditions. Coupled with a mass consumption society, there is a risk of detrimental social outcomes and threats to democracy, such as misinformation and political polarization. But AI, robotics and other emerging technologies could also provide a transition to community-based economies, in which more democratic, egalitarian, and sustainable value circulations can be established. Based on both a review of case studies, and our own experiments in Detroit, we derive three core principles for the use of computing in community-based economies. The prefigurative principle requires that the development process itself incorporates equity goals, rather than viewing equity as something to be achieved in the future. The generative principle requires the prevention of value extraction, and its replacement by circulations in which value is returned back to the aspects of labor, nature, and society by which it is generated. And third, the solidarity principle requires that deployments at all scales and across all domains support both individual freedoms and opportunities for mutual aid. Thus we propose the use of computational technologies to develop a specifically generative form of community-based economy: one that is egalitarian regarding race, class and gender; sustainable both environmentally and socially; and democratic in the deep sense of putting people in control of their own lives and livelihoods.
Outsourcing an Information Operation: A Complete Dataset of Tenet Media’s Podcasts on Rumble
arXiv, March 2025
Laura Kurek, Kevin Zheng, Eric Gilbert, Ceren Budak
Tenet Media, a U.S.-based, right-wing media company, hired six established podcasters to create content related to U.S. politics and culture during the 2024 U.S. presidential election cycle. After publishing content on YouTube and Rumble for nearly a year, Tenet Media was declared by the U.S. government to be funded entirely by Russia—making it effectively an outsourced state-sponsored information operation (SSIO). We present a complete dataset of the 560 podcast videos published by the Tenet Media channel on the video-sharing platform Rumble between November 2023 and September 2024. Our dataset includes video metadata and user comments, as well as high-quality video transcriptions, representing over 300 hours of video content. This dataset provides researchers with material to study a Russian SSIO, and notably on Rumble, which is an understudied platform in SSIO scholarship.
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