University 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 

The effect of trainee career intentions on mentor's interest in the trainee: Experimental evidence from academia

Inna Smirnova, Austin Shannon, Misha Teplitskiy

Research Policy, June 2025

In many industries trainees often seek careers different from their mentors. For example, many PhD students seek non-academic careers. Anecdotally, mentors invest less in different-career trainees, but causal evidence is lacking. To fill this gap, we conducted an audit experiment in academia, where a fictitious prospective PhD student emailed immunology and microbiology principal investigators (PIs) about mentorship. The student's career intention was randomly described as “applied research in industry” (n = 1000), “basic research in academia” (n = 1000) or no description (control, n = 442). To mitigate concerns about skills and motivation, all emails highlighted the student's great academic record. Contrary to expectations, PIs responded at similar rates across all conditions. Treatment effects showed little heterogeneity based on the PIs' institution prestige, industry connections, and career length. These null findings challenge the widespread belief that mismatched career intentions cause less mentorship (although the two may still be associated) and the mechanisms assumed to drive that effect. Our results call for caution in deploying interventions to fix problems related to advisor-mentee misalignments before clearly establishing their source.

To moderate, or not to moderate: Strategic domain sharing by congressional campaigns

Maggie Maddonald, Megan A. Brown, Joshua A. Tucker, Jonathan Nagler

Electoral Studies, June 2025 

We test whether candidates move to the extremes before a primary but then return to the center for the general election to appeal to the different preferences of each electorate. Incumbents are now more vulnerable to primary challenges than ever as social media offers a viable pathway for fundraising and messaging for challengers, while homogeneity of districts has reduced general election competitiveness. To assess candidates’ ideological trajectories, we estimate the messaging ideology of 2020 congressional campaigns before and after their primaries using a homophily-based measure of domains shared on Twitter. This method provides temporally granular data to observe changes in communication within a single election campaign cycle. We find suggestive evidence that incumbents in safe seats moved towards the extreme before their primaries and back towards the center for the general election, but only when threatened by a well-funded primary challenge.

The Role of Network and Identity in the Diffusion of Hashtags

International World Wide Web Conference, April 2025

Aparna Ananthasubramaniam, Yufei ‘Louise’ Zhu, David Jurgens, Daniel M. Romero

The diffusion of culture online is theorized to be influenced by many interacting social factors (e.g., network and identity). However, most existing computational cascade models consider just a single factor (e.g., network or identity). This work offers a new framework for teasing apart the mechanisms underlying hashtag cascades. We curate a new dataset of 1,337 hashtags representing cultural innovation online, develop a 10-factor evaluation framework for comparing empirical and simulated cascades, and show that a combined network+identity model better simulates hashtag cascades than network- or identity-only counterfactuals. We also explore heterogeneity in performance: While a combined network+identity model best predicts the popularity of cascades, a network-only model best predicts cascade growth and an identity-only model best predicts adopter composition. The network+identity model has the highest comparative advantage among hashtags used for expressing racial or regional identity and talking about sports or news. In fact, we are able to predict what combination of network and/or identity best models each hashtag and use this to further improve performance. Our results show the utility of models incorporating the interactions of network, identity, and other social factors in the diffusion of hashtags in social media.

Individual and collective transitions: Changes in family information networks over time in life with chronic illness

Journal of the Association for Information Science and Technology, March 2025

Lindsay K. Brown, Tiffany C. Veinot

Chronic illness represents a transition for both patients and their family members although transitions and information behavior changes have largely been explored from an individual perspective. Illness-related transitions may be undertaken individually or collectively, but little is known about how family information networks change in the face of either transition type. Therefore, we conducted a longitudinal, mixed-methods study of information networks with 28 families managing HIV or diabetes. Methods included qualitative interviews, surveys, and social network analysis. Findings revealed that transitions were common among study families, with collective transitions more common than individual ones. Network size changed more among families undergoing collective transition versus those with individual or no transitions. Collective transition families experienced slightly more tie strength increases than individual transition families. More families undergoing collective transitions had illness peers in the family network than individual or no-transition families. Reciprocal information sharing was also more common among illness peers. Findings support a distinction between individual and collective transitions and study of information network changes in each context. Future research should further characterize the drivers and dynamics of collective and individual transitions and related information behavior, while investigating how information systems and services can help.

Gallery walk as research method in information science

IConference, March 2025 

Rebecca Frank, Stephanie Krueger

Introduction. This paper argues that the gallery walks, a pedagogical technique traditionally used in educational settings, is an effective qualitative data collection strategy. Its interactive format stimulates discussion and ensures active participation, making it suitable for qualitative research.

Background. Common qualitative methods like interviews, focus groups, and observations have limitations. The gallery walk technique leverages movement and interaction to deepen understanding, making it an effective tool for comprehensive and inclusive educational engagement.

Gallery walk as research method. The gallery walk enhances data collection by balancing individual and group insights, encouraging participants to use their expertise and engage in meaningful discussions. This approach captures detailed information from each participant.

Case study. We implemented the gallery walk in a study with 14 experts in satellite image analysis. Over two days, participants engaged with six thematic stations, discussing and annotating posters. The discussions were audio-recorded and transcribed, providing a rich dataset of individual and collective insights.

Discussion and Conclusion. Our findings demonstrate the gallery walk's utility as a qualitative research method. Its structured yet flexible format enhances participant engagement and data richness. The gallery walk is particularly effective for studies involving expert participants, offering a comprehensive understanding of research topics.

Community Data and Situated Accountability

The Sage Handbook of Data and Society, February 2025 

Anita Say Chan & Patricia Garcia 

 Community data initiatives are collective efforts that work to advance accountability for data practices in ways that recognize the localizing harms resulting from extractive, punitive, segregating, and socially polarizing data systems. These initiatives advance institutional accountability in ways that support community self-determination and empower local forms of community agency in data practice and policy debates. In this chapter, we draw on the history of community data initiatives in social movements and activist traditions and four case studies to illustrate how community data initiatives function as situated forms of critical data work that counter harmful and violent datafication processes by: (1) highlighting the specific, localizing harms of datafication to diverse marginalized communities, while tying these harms to the historic exclusion of such communities in developing their work; (2) positing alternatives that reinvest in pluri-relational embodied forms of collective coexistence in the face of datafication and its stratifying impacts; and (3) activating data on community histories and lived experiences that prioritize community-accountable relations.

Toward Information Resilience: Applying Intersectionality to the HIV/AIDS Information Practices of Black Sexual Minority Men

Journal of the Association for Information Science and Technology, March 2025

Megan Threats

Using intersectionality as a critical theoretical framework and analytical tool, this study investigated the HIV/AIDS information practices of Black sexual minority men (SMM). Twenty-two Black SMM were interviewed about their HIV/AIDS-related information practices. The resulting data were analyzed inductively using methods influenced by constructivist grounded theory. I propose information resilience as a strengths-based concept to describe protective and promotive information practices that focus on meeting individual or community-centric goals despite intersectional stigma and discrimination. Anticipated and experienced intersectional stigma and discrimination were the key motivators for protective information practices among Black SMM. Promotive factors, including peer support and self-efficacy, shaped promotive information practices to foster development and enhance well-being. The findings have implications for the incorporation of intersectionality theory into information practices research, contribute to theoretical development in the field of library and information science, and have implications for the design of information and technology-based HIV prevention and treatment interventions to address intersectional discrimination and its impact on Black sexual minority men.

Demographic disparity in Wikipedia coverage: a global perspective

EPJ Data Science, February 2025

Yulin Yu, Xianglong Li, Tianyi Li, Paramveer S. Dhillon, Daniel M. Romero

Despite decades-long efforts to increase diversity, underrepresented social groups remain small minorities in many fields. Here, we ask whether disparities in global recognition exist for traditionally underrepresented demographic groups. We investigate whether a notable person’s demographic attributes are associated with their global recognition, considering both the global availability of public information about the person’s life and the consistency of such information. To track bibliographical information about notable people, we study Wikipedia, one of the most accessible knowledge bases on the Web. Using more than 1 million biographical articles from Wikipedia over ten years across the 12 largest language editions of Wikipedia, we study global gender and citizenship disparities in Wikipedia coverage. We measure global coverage in several ways, including the number of languages in which a person appears, the length of a person’s articles, and the global consensus about the person, which measures content similarity in the person’s articles across languages. We find that while females are broadly well-represented in terms of coverage in multiple languages starting from 2015, the quantity of the content of their articles and global consensus disparities persist consistently over time from 2010 to 2020. Additionally, some traditionally underrepresented nationalities are still covered less than their majority counterparts. Also, we observe an improvement on average in coverage while finding a persistent gender disparity in a specific domain, the global appearance of Olympic medal winners

Designing Courses for Liberal Arts and Sciences Students Contextualized around Creative Expression and Social Justice

SIGCSETS 2025, Proceedings of the 56th ACM Technical Symposium on Computer Science Education, February 2025

Mark Guzdial, Tamara Nelson-Fromm

The goal of teaching everyone computing (explicitly including programming) predates the definition of the computer science (CS) major and even the prospect of a software development career. At the University of Michigan, we are creating courses for non-CS majors which are grounded in the computational practices of liberal arts and sciences faculty. These courses have no connection to the CS major curriculum or software development jobs. We focus here on two of the themes that those faculty valued (Computing for Expression and Computing for Justice) and the introductory courses that we designed around each theme. The courses emphasize gaining broad perspectives of computing, which serve the study of multiple disciplines. Student activities include readings, writing essays, classroom discussion, and open-ended programming homework assignments. This experience report describes our design process, the Creative Expression and Social Justice courses, and an initial evaluation of our design. Most of the programming assignments were written in the block-based programming language Snap!, with some in-class exercises using teaspoon languages. Several units ended with an ebook assignment to connect the Snap! programming to equivalent programs in Python, Processing, and SQL. Interview and survey findings suggest that students found this sequence and the courses useful, despite not counting toward a CS major or focusing on early software development skills. Students described usefulness in terms of developing general computing knowledge, preparation for a range of future careers, and introducing them to other course choices.

Teaching Computing to K-12 Emergent Bilinguals: Identified Challenges and Opportunities

SIGCSETS 2025, Proceedings of the 56th ACM Technical Symposium on Computer Science Education, February 2025

Emma R. Dodoo, Tamara Nelson-Fromm, Mark Guzdial

Emergent bilingual (EB) students are a growing demographic within the United States, with an increasing number enrolling in K-12 computing courses. Since programming languages are primarily grounded in English, K-12 computing teachers must balance and tailor their instruction to meet the needs of these students. Teachers reported a lack of sufficient computing education resources to guide their instruction for teaching computing to EB students. Through a thematic analysis of semi-structured interviews with eight K-12 computing teachers who have EB students in their classrooms, we identified some of the challenges they face and the strategies they use to support them. Our analysis revealed three challenges: (1) students experience cognitive overload from translating between English and their native language, (2) terminology has subtle differences across disciplines (e.g., 'variable' in Math vs. Science), and (3) educators' low computing self-efficacy. Teachers counter these challenges with two implemented strategies: (1) providing multiple ways for EB students to engage with content to prevent them from becoming overwhelmed, and (2) offering multiple modalities to help translate computing concepts. This study contributes to the ongoing discussion on inclusive computing education by offering insights into educators' needs and potential solutions for supporting EB students' learning in computing.

Krupka-Weber norm elicitation method: a review

Elgar Encyclopedia of Behavioural and Experimental Economics, February 2025

Erin L. Krupka, Roberto A. Weber

Krupka and Weber's (2013) method for incentivised norm elicitation has been extensively used within the field of experimental economics to empirically estimate injunctive social norms. Due to its widespread use for measuring norms, subsequent work has explored and tested some of the methodological assumptions underlying the approach. Research has also tested how well the elicited norms track ex-ante identified norms and the robustness of the approach to competing focal points and response bias. The approach has been shown to be resilient to these concerns and remains an important methodology for the investigation of norms. Future work should continue to test novel affordances and limits of this method, such as the assumption that there exists a single, stable, commonly known norm, or the role that social networks play in the emergence, transmission, and maintenance of social norms.

Media Review

Journal of Mixed Methods Research, January 2025

Marcy G. Antonio

What motivates mixed methods research? What justifies mixed methods research? What guides mixed methods research? Yafeng Shan uses these three questions to introduce why multiple philosophical positions are found within mixed methods research (MMR). His edited book on the Philosophical Foundations of Mixed Methods Research: Dialogues between Researchers and Philosophers is divided into two parts. In the first section, mixed methods researchers from education and criminology present on seven approaches for MMR: pragmatist, transformative, post-colonial indigenous paradigm, dialectical stance, dialectical pluralism, performative and realist. In the second half, philosophers discuss how pragmatism, critical realism, casual ontology and evidential partnerships can contribute to the philosophical foundations of MMR. In the introduction, Shan brings his philosophical background to provide a critical synthesis of the chapters. He concludes the first chapter with future areas for exploring the philosophical foundations for MMR and emphasizes the importance of ongoing dialogue between researchers and philosophers throughout this exploration.

The Computing Research Association (CRA)’s Computing Community Consortium (CCC) and CRA-Industry (CRA-I) Response to the National Telecommunications and Information Administration (NTIA), Department of Commerce’s Request for Comments: Ethical Guidelines for Research Using Pervasive Data

Computer Research Association, January 2025

Nazanin Andalibi, David Danks, Haley Griffin, Mary Lou Maher, Jessica McClearn, Chinasa T. Okolo, Manish Parashar, Jessica Pater, Katie Siek, Tammy Toscos, Helen V. Wright, Pamela Wisniewski

This response is from Computing Research Association (CRA)’s Computing Community Consortium (CCC) and CRA-Industry (CRA-I). CRA is an association of nearly 250 North American computing research organizations, both academic and industrial, and partners from six professional computing societies. The mission of the CCC, a subcommittee of CRA, is to enable the pursuit of innovative, high-impact computing research that aligns with pressing national and global challenges. The mission of CRA-I, another subcommittee of CRA, is to convene industry partners on computing research topics of mutual interest and connect them with CRA’s academic and government constituents for mutual benefit and improved societal outcomes.

Pre-prints, Working Papers, Articles, Reports, Workshops and Talks

The Noisy Path from Source to Citation: Measuring How Scholars Engage with Past Research

arXiv, March 2025

Hong Chen, Misha Teplitskiy, David Jurgens

Academic citations are widely used for evaluating research and tracing knowledge flows. Such uses typically rely on raw citation counts and neglect variability in citation types. In particular, citations can vary in their fidelity as original knowledge from cited studies may be paraphrased, summarized, or reinterpreted, possibly wrongly, leading to variation in how much information changes from cited to citing paper. In this study, we introduce a computational pipeline to quantify citation fidelity at scale. Using full texts of papers, the pipeline identifies citations in citing papers and the corresponding claims in cited papers, and applies supervised models to measure fidelity at the sentence level. Analyzing a large-scale multidisciplinary dataset of approximately 13 million citation sentence pairs, we find that citation fidelity is higher when authors cite papers that are 1) more recent and intellectually close, 2) more accessible, and 3) the first author has a lower H-index and the author team is mediumsized. Using a quasi-experiment, we establish the "telephone effect" – when citing papers have low fidelity to the original claim, future papers that cite the citing paper and the original have lower fidelity to the original. Our work reveals systematic differences in citation fidelity, underscoring the limitations of analyses that rely on citation quantity alone and the potential for distortion of evidence.

Efficient Estimation of Shortest-Path Distance Distributions to Samples in Graphs

arXiv, February 2025

Alan Zhu, Jiaqi Ma, Qiaozhu Mei

As large graph datasets become increasingly common across many fields, sampling is often needed to reduce the graphs into manageable sizes. This procedure raises critical questions about representativeness as no sample can capture the properties of the original graph perfectly, and different parts of the graph are not evenly affected by the loss. Recent work has shown that the distances from the non-sampled nodes to the sampled nodes can be a quantitative indicator of bias and fairness in graph machine learning. However, to our knowledge, there is no method for evaluating how a sampling method affects the distribution of shortest-path distances without actually performing the sampling and shortest-path calculation. In this paper, we present an accurate and efficient framework for estimating the distribution of shortest-path distances to the sample, applicable to a wide range of sampling methods and graph structures. Our framework is faster than empirical methods and only requires the specification of degree distributions. We also extend our framework to handle graphs with community structures. While this introduces a decrease in accuracy, we demonstrate that our framework remains highly accurate on downstream comparison based tasks. Code is publicly available at https://github.com/az1326/ shortest_paths.

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