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.

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Teach LLMs to Personalize – An Approach inspired by Writing Education

arXiv, August 2023

Cheng Li, Mingyang Zhang, Qiaozhu Mei, Yaqing Wang, Spurthi Amba Hombaiah, Yi Liang, Michael Bendersky

Personalized text generation is an emerging research area that has attracted much attention in recent years. Most studies in this direction focus on a particular domain by designing bespoke features or models. In this work, we propose a general approach for personalized text generation using large language models (LLMs). Inspired by the practice of writing education, we develop a multistage and multitask framework to teach LLMs for personalized generation. In writing instruction, the task of writing from sources is often decomposed into multiple steps that involve finding, evaluating, summarizing, synthesizing, and integrating information. Analogously, our approach to personalized text generation consists of multiple stages: retrieval, ranking, summarization, synthesis, and generation. In addition, we introduce a multitask setting that helps the model improve its generation ability further, which is inspired by the observation in education that a student’s reading proficiency and writing ability are often correlated. We evaluate our approach on three public datasets, each of which covers a different and representative domain. Our results show significant improvements over a variety of baselines.

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RunEx: Augmenting Regular-Expression Code Search with Runtime Values

The IEEE Symposium on Visual Languages and Human-Centric Computing, October 2023

Ashley Ge Zhang, Yan Chen, Steve Oney

Programming instructors frequently use in-class exercises to help students reinforce concepts learned in lecture. However, identifying class-wide patterns and mistakes in students’ code can be challenging, especially for large classes. Conventional code search tools are insufficient for this purpose as they are not designed for finding semantic structures underlying large students’ code corpus, where the code samples are similar, relatively small, and written by novice programmers. To address this limitation, we introduce RunEx, a novel code search tool where instructors can effortlessly generate queries with minimal prior knowledge of code search and rapidly search through a large code corpus. The tool consists of two parts: 1) a syntax that augments regular expressions with runtime values, and 2) a user interface that enables instructors to construct runtime and syntax-based queries with high expressiveness and apply combined filters to code examples. Our comparison experiment shows that RunEx outperforms baseline systems with text matching alone in identifying code patterns with higher accuracy. Furthermore, RunEx features a user interface that requires minimal prior knowledge to create search queries. Through searching and analyzing students’ code with runtime values at scale, our work introduces a new paradigm for understanding patterns and errors in programming education. 

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The Shapes of the Fourth Estate During the Pandemic: Profiling COVID-19 News Consumption in Eight Countries

CSCW, August 2023

Cai Yang, Lexing Xie, Siqi Wu

News media is often referred to as the Fourth Estate, a recognition of its political power. New understandings of how media shape political beliefs and influence collective behaviors are urgently needed in an era when public opinion polls do not necessarily reflect election results and users influence each other in real-time under algorithm-mediated content personalization. In this work, we measure not only the average but also the distribution of audience political leanings for different media across different countries. The methodological components of these new measures include a high-fidelity COVID-19 tweet dataset; high-precision user geolocation extraction; and user political leaning estimated from the within-country retweet networks involving local politicians. We focus on geolocated users from eight countries, profile user leaning distribution for each country, and analyze bridging users who have interactions across multiple countries. Except for France and Turkey, we observe consistent bimodal user leaning distributions in the other six countries, and find that cross-country retweeting behaviors do not oscillate across the partisan divide. More importantly, this study contributes a new set of media bias estimates by averaging the leaning scores of users who share the URLs from media domains. Through two validations, we find that the new average audience leaning scores strongly correlate with existing media bias scores. Lastly, we profile the COVID-19 news consumption by examining the audience leaning distribution for top media in each country, and for selected media across all countries. Those analyses help answer questions such as: Does center media Reuters have a more balanced audience base than partisan media CNN in the U.S.? Does far-right media Breitbart attract any left-leaning readers in any countries? Does CNN reach a more balanced audience base in the U.S. than in the U.K.?

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Wealthy Americans and redistribution: The role of fairness preferences

Journal of Public Economics, September 2023

Alain Cohn, Lasse J. Jessen, Marko Klasnja, Paul Smeets

We examine the attitudes of the wealthy towards government redistribution using a large and diverse sample of individuals from the top 5% of the income and wealth distribution in the U.S., as well as the remaining 95%. Three results stand out: (1) wealthy Americans have distinct fairness preferences, with a greater willingness to accept inequalities relative to the general public, (2) individuals who self-report having experienced upward social mobility and became first-generation wealthy are particularly accepting of inequality, while those born into wealth have fairness preferences similar to the general population; (3) the disparity in fairness preferences between the rich and the general public is predictive of greater opposition to redistribution among the wealthy, resulting in more conservative voting behavior. These findings provide new insights into the reasons behind the wealthy’s opposition to government redistribution.

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Profile Update: The Effects of Identity Disclosure on Network Connections and Language

arXiv, August 2023

Minje Choi, Daniel Romero, David Jurgens

Our social identities determine how we interact and engage with the world surrounding us. In online settings, individuals can make these identities explicit by including them in their public biography, possibly signaling a change to what is important to them and how they should be viewed. Here, we perform the first large-scale study on Twitter that examines behavioral changes following identity signal addition on Twitter profiles. Combining social networks with NLP and quasi-experimental analyses, we discover that after disclosing an identity on their profiles, users (1) generate more tweets containing language that aligns with their identity and (2) connect more to same-identity users. We also examine whether adding an identity signal increases the number of offensive replies and find that (3) the combined effect of disclosing identity via both tweets and profiles is associated with a reduced number of offensive replies from others.

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Communication is a Two-Way Street: Negotiating Driving Intent through a Shape-Changing Steering Wheel

2023 IEEE World Haptics Conference, July 2023 

Hannah Baez, Akshay Bhardwaj, Jean Costa, John Gideon, Sile O’Modhrain, Nadine Sarter, Brent Gillespie

In this information age, our machines have evolved from tools that process mechanical work into computerized devices that process information. A collateral outcome of this trend is a diminishing role for haptic feedback. If the benefits of haptic feedback, including those inherent in tool use, are to be preserved in information processing machines, we require an improved understanding of the various ways in which haptic feedback supports embodied cognition and supports high utility exchange of information. In this paper we classify manual control interfaces as instrumental or semiotic and describe an exploratory study in which a steering wheel functions simultaneously to communicate tactical and operational features in semi-autonomous driving. A shape-changing interface (semiotic/tactical) in the grip axis complements haptic shared control (instrumental/operational) in the steering axis. Experimental results involving N=30 participants show that the addition of a semiotic interface improves human automation team performance in a shared driving scenario with competing objectives and metered information sharing.

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Equity in virtual care: A mixed methods study of perspectives from physicians

Journal of Telemedicine and Telecare, August 2023

Timothy C Guetterman, Emily Koptyra, Olivia Ritchie, Liz B Marquis, Reema Kadri, Anna Laurie, VG Vinod Vydiswaran, Jiazhao Li, Lindsay K Brown, Tiffany C Veinot, and Lorraine R Buis

Background: Virtual care expanded rapidly during the COVID-19 pandemic, and how this shift affected healthcare disparities among subgroups of patients is of concern. Racial and ethnic minorities, older adults, individuals with less education, and lower-income households have lower rates of home broadband, smartphone ownership, and patient portal adoption, which may directly affect access to virtual care. Because primary care is a major access point to healthcare, perspectives of primary care providers are critical to inform the implementation of equitable virtual care.

Objective: The aim of this mixed methods study was to explore primary care physician experiences and perceptions of barriers and facilitators to equitable virtual care.

Design: We used an explanatory sequential mixed methods design, which consists of first collecting and analyzing quantitative survey data, then using those results to inform a qualitative follow-up phase to explain and expand on results.

Participants: Primary care physicians in a family medicine department at an academic medical center responded to surveys (n = 38) and participated in interviews (n = 16).

Approach: Participants completed a survey concerning frequency and preferences about video visits, pros and cons of video visits, communication aspects, and sufficiency of the technology. A purposeful sample of participants completed semi-structured interviews about their virtual care experiences with a focus on equity for subpopulations.

Key Results: The results indicated that physicians have observed equity issues for unique patient populations. The results add to the understanding of nuanced ways in which virtual care can increase and decrease healthcare access for unique populations. Patients with limited English proficiency were particularly affected by inequity in virtual care access.

Conclusion: Additional research and interventions are needed to improve portal access for those with limited English proficiency. Improvements should focus on health system interventions that expand access without requiring increased patient burden.

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A Text Messaging Intervention for Priming the Affective Rewards of Exercise in Adults: Protocol for a Microrandomized Trial

JMIR Research Protocols, January 2023

Sonali R Mishra, Walter Dempsey, Predrag Klasnja

Background: Physical activity is a critical target for health interventions, but effective interventions remain elusive. A growing body of work suggests that interventions targeting affective attitudes toward physical activity may be more effective for sustaining activity long term than those that rely on cognitive constructs alone, such as goal setting and self-monitoring. Anticipated affective response in particular is a promising target for intervention. 

Objective: We will evaluate the efficacy of an SMS text messaging intervention that manipulates anticipated affective response to exercise to promote physical activity. We hypothesize that reminding users of a positive postexercise affective state before their planned exercise sessions will increase their calories burned during this exercise session. We will deploy 2 forms of affective SMS text messages to explore the design space: low-reflection messages written by participants for themselves and high-reflection prompts that require users to reflect and respond. We will also explore the effect of the intervention on affective attitudes toward exercise. 

Methods: A total of 120 individuals will be enrolled in a 9-week microrandomized trial testing affective messages that remind users about feeling good after exercise (40% probability), control reminders (30% probability), or no message (30% probability). Two types of affective SMS text messages will be deployed: one requiring a response and the other in a read-only format. Participants will write the read-only messages themselves to ensure that the messages accurately reflect the participants’ anticipated postexercise affective state. Affective attitudes toward exercise and intrinsic motivation for exercise will be measured at the beginning and end of the study. The weighted and centered least squares method will be used to analyze the effect of delivering the intervention versus not on calories burned over 4 hours around the time of the planned activity, measured by the Apple Watch. Secondary analyses will include the effect of the intervention on step count and active minutes, as well as an investigation of the effects of the intervention on affective attitudes toward exercise and intrinsic motivation for exercise. Participants will be interviewed to gain qualitative insights into intervention impact and acceptability. 

Results: Enrollment began in May 2023, with 57 participants enrolled at the end of July 2023. We anticipate enrolling 120 participants. 

Conclusions: This study will provide early evidence about the effect of a repeated manipulation of anticipated affective response to exercise. The use of 2 different types of messages will yield insight into optimal design strategies for improving affective attitudes toward exercise.

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Mining Semantic Relations in Data References to Understand the Roles of Research Data in Academic Literature

Proceedings of the ACM/IEEE Joint Conference on Digital Libraries, September 2023

Lizhou Fan, Sara Lafia, Morgan Wofford, Andrea Thomer, Elizabeth Yakel, Libby Hemphill 

Research data serves important roles in scientific discovery and academic innovation. To appropriately assign credit for data work and to measure the value of research data, it is essential to articulate how data are actually used in research. We leveraged a combination of computational methods and human analysis to characterize different types of data use by mining semantic relations from the phrases where data are referenced in academic literature. In particular, we investigated references to data in the bibliography of a large social science data archive, the Inter-university Consortium for Political and Social Research (ICPSR). After retrieving and extracting semantic relations as subject-relation-object triples, we used rule-based methods to classify them. We then annotated samples from 11 frequent classes of data reference triples and found that they vary primarily along two dimensions of data use: proximity and function. Proximity describes the distance between the author and the data they reference (e.g., direct or indirect engagement). Function describes the role that data plays in each reference (e.g., describing interaction or providing context). These semantic relationships between authors and data reveal the ways data are used in scientific publications. Evidence of the variety of ways data are used can help stakeholders in research data curation and stewardship – including data providers, data curators, and data users – recognize the myriad ways that their investments in data sharing are realized.

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Community-Engaged Participatory Methods to Address LGBTQ+ Young People’s Health Information Needs With a Resource Website: Participatory Design and Development Study

JMIR Formative Research, September 2023 

Daniel Delmonaco, Shannon Li, Christian Paneda, Elliot Popoff, Luna Hughson, Laura Jadwin-Cakmak, Jack Alferio, Christian Stephenson, Angelique Henry, Kiandra Powdhar, Isabella Gierlinger, Gary W Harper, Oliver L Haimson

Background: Lesbian, gay, bisexual, transgender, queer, and questioning (LGBTQ+) young people (aged 15 to 25 years) face unique health challenges and often lack resources to adequately address their health information needs related to gender and sexuality. Beyond information access issues, LGBTQ+ young people may need information resources to be designed and organized differently compared with their cisgender and heterosexual peers and, because of identity exploration, may have different information needs related to gender and sexuality than older people. 

Objective: The objective of our study was to work with a community partner to develop an inclusive and comprehensive new website to address LGBTQ+ young people’s health information needs. To design this resource website using a community-engaged approach, our objective required working with and incorporating content and design recommendations from young LGBTQ+ participants. 

Methods: We conducted interviews (n=17) and participatory design sessions (n=11; total individual participants: n=25) with LGBTQ+ young people to understand their health information needs and elicit design recommendations for the new website. We involved our community partner in all aspects of the research and design process. 

Results: We present participants’ desired resources, health topics, and technical website features that can facilitate information seeking for LGBTQ+ young people exploring their sexuality and gender and looking for health resources. We describe how filters can allow people to find information related to intersecting marginalized identities and how dark mode can be a privacy measure to avoid unwanted identity disclosure. We reflect on our design process and situate the website development in previous critical reflections on participatory research with marginalized communities. We suggest recommendations for future LGBTQ+ health websites based on our research and design experiences and final website design, which can enable LGBTQ+ young people to access information, find the right information, and navigate identity disclosure concerns. These design recommendations include filters, a reduced number of links, conscientious choice of graphics, dark mode, and resources tailored to intersecting identities. 

Conclusions: Meaningful collaboration with community partners throughout the design process is vital for developing technological resources that meet community needs. We argue for community partner leadership rather than just involvement in community-based research endeavors at the intersection of human-computer interaction and health. 

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TikTok as algorithmically mediated biographical illumination: Autism, self-discovery, and platformed diagnosis on #autisktok

Journal of New Media & Society, August 2023

Meryl Alper, Jessica Sage Rauchberg, Ellen Simpson, Josh Guberman, Sarah Feinberg

Scholarship in the sociology of medicine has tended to characterize diagnosis as disruptive to one’s self-concept. This categorization, though, requires reconsideration in light of public conversations about mental health and community building around neurocognitive conditions, particularly among youth online. Drawing upon Tan’s notion of “biographical illumination” (BI), which describes how medical frameworks can enrich personal biographies, we explored the shifting nature of BI through the case of TikTok. Combining quantitative and qualitative methods, we argue that TikTok serves as a space to discuss diagnosis and refine one’s sense of self as a result of diagnosis. However, such personal transformation is inseparable from the app’s affordances, or what we term “algorithmically mediated biographical illumination.” BI shapes TikTok as a platform, and TikTok informs BI as a psychosocial process, leading to what we call “platformed diagnosis.” These findings have broader critical applications for the study of algorithms, disability, and digital platforms.

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The Impact of Modality, Technology Suspicion, and NDRT Engagement on the Effectiveness of AV Explanations

IEEE, September 2023

Qiaoning Zhang, Connor Esterwood, Anuj K Pradhan ,Dawn Tilbury, X. Jessie Yang, Lionel P. Robert

Explanations — reasons or justifications for action — are being used to promote the acceptance of automated vehicles (AVs). Yet, it is unclear whether and how the modality of explanation affects its effectiveness. Despite its importance in the technology acceptance literature, the impact of technology suspicion on the adoption of AVs is yet to be fully examined. To expand our understanding of AV explanation, we conducted a within-subjects experiment with 32 participants using a high-fidelity driving simulator. Four conditions were presented to participants: (1) auditory explanation with a non-driving-related task (NDRT), (2) auditory explanation without NDRT, (3) visual explanation with NDRT, and (4) visual explanation without NDRT. The results indicate that auditory explanations are more effective in reducing anxiety and unsafety perception for high-suspicion individuals, especially in the absence of NDRT. Conversely, individuals who are less technology suspicious prefer visual explanations, which can result in lower levels of anxiety and perceived unsafety. The study highlights the importance of considering individuals’ technology suspicion and engagement with NDRT when selecting the appropriate explanation modality, and the findings can guide the design of future AV systems to promote effective human-machine interaction.

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Human Security Robot Interaction and Anthropomorphism: An Examination of Pepper, RAMSEE, and Knightscope Robots

Proceedings of the 32nd IEEE International Conference on Robot and Human Interactive Communication, August 2023

Xin Ye, Lionel P. Robert

The rapid growth in the use of security robots makes it critical to better understand their interactions with humans. The impacts of anthropomorphism and interaction scenarios were examined via a 3 x 2 between-subjects experiment. Sixty participants were randomly assigned to interact with one of three security robots (Knightscope, RAMSEE, or Pepper) in either an indoor hallway or an outdoor parking lot scenario in a virtual reality cave. There were significant differences only between Pepper and Knightscope with Pepper rated higher in anthropomorphism, ability, integrity, and desire to use than Knightscope but the interaction scenario has no effect.

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Psychometric Evaluation of the Modes of Health Information Acquisition, Sharing, and Use Questionnaire: Prospective Cross-Sectional Observational Study

Journal of Medical Internet Research, September 2023

Lenette M Jones, Ronald J Piscotty Jr,  Stephen Sullivan, Beatriz Manzor Mitrzyk, Robert J Ploutz-Snyder, Bidisha Ghosh, Tiffany Veinot

Background: Health information is a critical resource for individuals with health concerns and conditions, such as hypertension. Enhancing health information behaviors may help individuals to better manage chronic illness. The Modes of Health Information Acquisition, Sharing, and Use (MHIASU) is a 23-item questionnaire that measures how individuals with health risks or chronic illness acquire, share, and use health information. Yet this measure has not been psychometrically evaluated in a large national sample. Objective: The objective of this study was to evaluate the psychometric properties of the self-administered MHIASU in a large, diverse cohort of individuals living with a chronic illness. Methods: Sharing Information, a prospective, observational study, was launched in August 2018 and used social media campaigns to advertise to Black women. Individuals who were interested in participating clicked on the advertisements and were redirected to a Qualtrics eligibility screener. To meet eligibility criteria individuals had to self-identify as a Black woman, be diagnosed with hypertension by a health care provider, and live in the United States. A total of 320 Black women with hypertension successfully completed the eligibility screener and then completed a web-based version of the MHIASU questionnaire. We conducted a psychometric evaluation of the MHIASU using exploratory factor analysis. The evaluation included item review, construct validity, and reliability. Results: Construct validity was established using exploratory factor analysis with principal axis factoring. The analysis was constricted to the expected domains. Interitem correlations were examined for possible item extraction. There were no improvements in factor structure with the removal of items with high interitem correlation (n=3), so all items of the MHIASU were retained. As anticipated, the instrument was found to have 3 subscales: acquisition, sharing, and use. Reliability was high for all 3 subscales, as evidenced by Cronbach α scores of .81 (acquisition), .81 (sharing), and .93 (use). Factor 3 (use of health information) explained the maximum variance (74%). Conclusions: Construct validity and reliability of the web-based, self-administered MHIASU was demonstrated in a large national cohort of Black women with hypertension. Although this sample was highly educated and may have had higher digital literacy compared to other samples not recruited via social media, the population captured (Black women living with hypertension) are often underrepresented in research and are particularly vulnerable to this chronic condition. Future studies can use the MHIASU to examine health information behavior in other diverse populations managing health concerns and conditions.