Coordinating Chaos | Clickbait | Political identification: UMSI Research Roundup
Thursday, 09/18/2025
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
Mobilizing under uncertainty: Political identification, resource activation, and technology adoption among necessity entrepreneurs
Journal of Business Venturing Insights, November 2025
Amrita Lahiri, Alexander Kier, Nanjundi Karthick Krishnan, Aditya Johri, Joyojeet Pal
How does political identification shape entrepreneurial action in the wake of a major policy shock? We investigate this question using survey data from 294 necessity entrepreneurs following India’s 2016 demonetization—a disruptive policy that promoted digital payment technologies as a state- endorsed solution. We examine technology adoption as an entrepreneurial response to institutional uncertainty, focusing on how political identification shapes the mobilization of financial and human capital. We find that entrepreneurs aligned with the ruling party were more likely to activate their resources and adopt digital payment technology. By illustrating how identity-driven cognition reduces perceived ambiguity in opportunity evaluation and promotes entrepreneurs’ willingness to act, this study offers new insights into entrepreneurial action under institutional uncertainty.
Medical Information Provided by Transgender and Gender-Diverse Content Creators on YouTube: Descriptive Content Analysis
JMIR Formative Research, August 2025
Lydia Bliss, Qianqian Zhao, Irene Chao, Oliver L Haimson, Ellen Selkie
Background: Transgender and gender-diverse (TGD) individuals frequently turn to social media to find community, express their identities, and access essential information. These platforms are easily accessible to TGD people and enable health information–seeking in anonymous, identity-affirming spaces outside of traditional health care systems. As a result, social media has become a critical source of health information on topics like gender-affirming care for TGD individuals, specifically for TGD youth. YouTube, one of the most widely used social media platforms, is especially popular for its long-form videos made by content creators who have built dedicated followings on the platform. Among them are TGD content creators, many of whom make content documenting their medical transition and gender identity journey and provide general information about TGD topics. TGD creator content therefore makes YouTube an important platform for health education for TGD individuals.
Objective: This study aims to describe the health-related content shared by TGD content creators on YouTube. Specifically, we characterize the medical topics addressed, the frameworks used to discuss these topics, and the valence of creators’ health care experiences.
Methods: A descriptive content analysis was performed on 2485 videos posted by 42 self-identified TGD YouTube content creators. Videos were systematically evaluated for mentions of gender-affirming care and other health-related topics. We also examined whether creators framed medical information using personal narratives or an informational approach and if they characterized their medical experiences as positive, negative, or neutral.
Results: Most videos (n=1724, 69.4%) created by TGD content creators did not include discussions related to gender identity or transitioning. However, among the videos that did address gender identity (n=761, 30.6%), mentions of medical topics were prevalent (n=554, 72.8%). Of videos that discussed medical topics, gender-affirming surgeries (n=356, 64.3%) and hormone replacement therapy (n=307, 55.4%) were the most frequently discussed. Other commonly discussed medical topics included mental health (n=131, 23.6%) and sexual health (n=96, 17.3%). Videos covering medical topics primarily centered on personal experiences (n=411, 74.2%), with content creators often characterizing these experiences positively (n=224, 73.2%).
Conclusions: This study highlights the breadth of health-related information shared by TGD content creators on YouTube. Our findings underscore the role of long-form video content on YouTube as an educational resource for TGD people, offering health information that is both easy to access and grounded in lived experience. Clinicians can use these findings to better understand the health information that their TGD clients are likely to encounter online, fostering more informed and supportive conversations about gender-affirming care.
Timing and cross-platform presence shape the online dissemination of science
EPG Data Science, August 2025
José Córdova, Emőke-Ágnes Horvát, Daniel M. Romero
The increasing reliance on digital media has shifted the discussion and dissemination of academic findings to various online platforms, such as news sites, blogs, and social media, making the World Wide Web the primary source of scientific information for the broader population. However, a comprehensive understanding of the dissemination of academic articles online, especially across multiple platforms, remains limited. In this study, we examine the online dissemination patterns of academic articles by analyzing a dataset of ∼18 million posts from diverse sources that mention close to 430k articles. We employ a clustering-based approach on the time series of online mentions of research articles to discern dissemination trajectories, identifying common patterns across articles. Furthermore, these trajectories are also associated with the future academic citation count. These associations hold even after controlling for the total number of mentions, underscoring the importance of how mentions are distributed across platforms and over time, rather than just their overall volume.
Geographical diversity of peer reviewers shapes author success
Proceedings of the National Academy of Sciences of the United States of America, August 2025
James M. Zumel Dumlao, Misha Teplitskiy
Scientific institutions like funding agencies and journals rely on peer reviewers to select among competing submissions. How does the geographical diversity of reviewers affect which authors are selected? If reviewers typically favor submissions from their own countries, but reviewers from only some countries are well represented in the reviewer pool, this can create a “geographical representation bias” favoring authors from those well-represented countries. Using administrative data on 204,718 submissions to 60 STEM journals from the Institute of Physics Publishing, we find support for representation bias. Reviewers from the same country as the corresponding author are 4.78 percentage points more likely to review positively compared to other reviewers of the same manuscript. Authors from the United States of America, China, and India are 8 to 9 times more likely to be evaluated by same-country reviewers compared to less-represented countries with similar incomes. Furthermore, an instrumental variables analysis of an anonymization policy shock shows that anonymizing submissions does not significantly reduce same-country homophily. Thus, investments in reviewer diversification may be necessary to mitigate the structural advantage of authors from major science-producing countries and avoid blind spots in collective knowledge.
Improving Affective Associations With Physical Activity via a Message-Based mHealth Intervention (WalkToJoy): Proof-of-Concept Study
Journal of Medical Internet Research, August 2025
Soo Ji Serisse Choi, Pei-Yao Hung, Mengyun Liu, Walter Dempsey, Mark W Newman, Predrag Klasnja
Background: Traditional mobile health interventions for physical activity (PA) primarily rely on reflective self-regulatory processes, often neglecting the role of affective associations in sustaining long-term engagement. The WalkToJoy intervention addresses this gap by applying the affective-reflective theory to enhance intrinsic motivation for PA among adults aged ≥40 years through affective message framing, evaluative conditioning, and belief updating.
Objective: This proof-of-concept study evaluated the feasibility of the message-based WalkToJoy intervention package and examined the impact of its 3 components—walking suggestion prompts, salience messages, and planning prompts—on affective and behavioral outcomes related to walking.
Methods: We conducted a fully remote, 6-week full factorial experiment with an embedded microrandomized trial (MRT) involving 49 adults aged ≥40 years. Statistical analyses, including paired t tests and generalized estimating equations, assessed pretest-posttest changes and the effects of smile-inducing walking suggestion prompts with short animated images (GIF images), salience messages, and planning prompts on weekly affective measures and daily step counts. In addition, MRT analyses evaluated the proximal effects of these components. Poststudy interviews were thematically analyzed to contextualize participants’ experiences and engagement with the intervention.
Results: Significant pretest-posttest improvements were observed across affective outcomes on a 7-point Likert scale—affective attitudes improved by 0.547 points (P<.001), affective valuations improved by 0.718 points (P<.001), affective reflection improved by 0.692 points (P<.001), and anticipated affect improved by 0.692 points (P<.001). While the average daily steps showed a nonsignificant pretest-posttest increase of 80 steps (P=.79), further analysis revealed an increase of 506 steps (P=.07) when comparing baseline to the average of weeks 4 to 6. Among the intervention components, GIF prompts significantly increased anticipated affect by 0.345 points (P=.046) and average daily step count by 1834 steps (P=.05) compared to identical text-only prompts. However, MRT analysis found no significant increase in 4-hour step counts following the walking suggestion prompts (P=.55), which was explained by qualitative findings suggesting that participants interpreted messages as flexible day-long reminders rather than immediate calls to action. Salience and planning prompts did not yield substantial quantitative effects but were positively received by participants for promoting mindfulness and personalized engagement.
Conclusions: The WalkToJoy intervention is a feasible and promising approach for improving affective associations with walking. Walking suggestion prompts were particularly effective in boosting engagement and mitigating message fatigue, highlighting the potential of affect-driven interventions to enhance PA motivation and adherence.
One Platform, Four Languages: Comparing English, Spanish, Hindi, and Russian YouTube
Social Media and Society, August 2025
Ryan McGrady, Kevin Zheng, Ethan Zuckerman
This study presents a comparative analysis of language-specific random samples of YouTube videos, focusing on English, Spanish, Hindi, and Russian. We produce a large random sample, retrieve metadata, calibrate and deploy language-detection software, and extract four high-confidence language samples. Through an analysis of upload dates, popularity, duration, and category metadata, we highlight patterns and anomalies among our samples. For example, English YouTube has the smallest proportion of videos categorized as “News & Politics,” and Spanish videos have a longer median duration. The most salient contrast, however, is between Hindi YouTube and the other three languages. Hindi videos are much shorter and much newer, with sharp growth since 2020 and more than half of the sample uploaded in 2023 alone. The Hindi sample also exhibits a different pattern of liking, with the lowest percentage of videos with just zero or one like even while it has the highest percentage of videos with just zero or one view. These findings may help to quantify the migration of India’s short-form video culture, based around TikTok, to YouTube when TikTok was banned in the country in 2020. This study underscores the necessity of multilingual and culturally specific approaches to platform research by drawing attention to the heterogeneity of YouTube. We propose this method as a starting point to understand linguistic communities on YouTube, surfacing trends and exceptions while providing cues for more content-focused study.
Between Court Orders and Platform Policies: Understanding Law Enforcement and Meta Interactions in Addressing Non-Consensual Image Disclosure Abuse
Twenty-First Symposium on Usable Privacy and Security, August 2025
Non-Consensual Image Disclosure Abuse (NCIDA) occurs when one person posts, or threatens to post, sensitive images of another person online with the intent to extort, humiliate, or harm. Though much is known about NCIDAs, almost nothing is known about how law enforcement agencies (LEAs) work with social media companies to address them, especially outside the West. Through discussions with Pakistani law enforcement, and legal experts, and analysis of LEA requests submitted to Meta platforms, we find that platforms are reasonably proactive in responding to NCIDA-related requests. However, their decisions are seem to be heavily influenced by their universal content-moderation policies, which are determined by Western norms that prioritize sexually explicit content but neglect content considered sensitive in other cultures. Our findings contribute a nuanced understanding of the communication between LEAs and social media companies in combating NCIDA, and lead to recommendations for platforms and government policy in mitigating NCIDA.
Scholarly Disengagement as an Epistemic Crisis: Clickbait, Credibility, and the Decline of Public-Facing Science
CI '25: Proceedings of the ACM Collective Intelligence Conference, August 2025
Maalvika Bhat, Daniel Romero, Emőke-Ágnes Horvát
How do scholars navigate media engagement in an era where digital incentives prioritize virality over scientific accuracy? This study examines how career stage and discipline influence academics’ willingness to engage with journalists, using survey responses from 5,603 U.S.-based researchers. Findings reveal a gap in science communication: at least 13.8% (n=774) of scholars in our sample voluntarily reported withdrawing from media engagement entirely. Disengagement was more prevalent among later-career scholars and researchers in Health, Political, and Environmental Science. Thematic analysis of open-ended responses uncovers scholars’ mediafacing protective strategies, ranging from editorial oversight to full withdrawal, and documents recurring concerns around misrepresentation, reputational risk, and visibility pressure. These patterns suggest that media withdrawal is often a learned response to recurring challenges in science communication. This study identifies patterns of expert withdrawal that may affect the epistemic diversity of voices in public science communication.
Optimizing and Testing an Individualized and Adaptive Physical Activity Digital Health Intervention: Protocol for a Control Optimization Trial Embedded Within a Randomized Controlled Trial
JMIR Research Protocols, August 2025
Meelim Kim, Shadia Mansour-Assi, Mohamed El Mistiri, Junghwan Park, Sarasij Banerjee, Owais Khan, Steven De La Torre, Michael Higgins, Job Godino, Kevin Patrick, Camille Nebeker, Sonia Jain, Predrag Klasnja, Daniel E Rivera, Eric Hekler
Background: While effective physical activity (PA) interventions exist, interventions often work only for some individuals or only for a limited time. Thus, there is a need for digital health interventions that account for dynamic, idiosyncratic PA determinants to support each person’s PA. We hypothesize that supporting individuals with their personal PA goals requires a personalized intervention that both supports each person in forming daily habits of walking more and develops personalized knowledge, skills, and practices regarding engaging in exercise routines. We operationalized these adaptive features via a digital health intervention called YourMove that uses a control systems approach to support personalized habit formation and a self-experimentation approach to develop personalized knowledge, skills, and practices.
Objective: The primary aim is to evaluate differences in minutes of moderate to vigorous PA (MVPA) per week at 12 months comparing our personalized intervention, called YourMove, with an active control that is similar but without personalization of the intervention components and mimics best-in-class digital health worksite wellness programs.
Methods: The YourMove study is a 12-month randomized controlled trial that involves 386 inactive adults aged 25 to 80 years. All participants receive (1) a Fitbit Versa smartwatch and corresponding smartphone app; (2) weekly PA goal suggestions and feedback, behavior change strategies, and reminders via SMS text messaging; and (3) up to US $50 in incentives for reaching daily step goals. Participants randomized to the active control group, modeled after worksite wellness programs, receive all the elements described in addition to a static daily step goal and static point rewards. Participants randomized to the intervention group receive (1) a habit formation element with daily personalized step goals and personalized point rewards generated through a control optimization trial approach and (2) a knowledge, skill, and practice development element featuring a self-guided self-experimentation tool that helps individuals find strategies to improve MVPA. The primary outcome is objectively assessed weekly minutes of MVPA via an ActiGraph monitor.
Results: Recruitment began in October 2022 and concluded in August 2024. Data collection will conclude in August 2025, with results expected by early 2026.
Conclusions: We hypothesize that the intervention group will show greater improvement in MVPA than the active control group at 12 months. If the hypothesis is supported, this will provide compelling evidence to suggest that personalized and perpetually adaptive support can enhance PA more effectively than intervention elements commonly used in digital health worksite wellness programs. If the trial is successful, the results will provide justification to explore both the control optimization trial approach and self-experimentation approach for other complex, idiosyncratic, and dynamic behaviors such as weight management, smoking, or substance abuse.
Multiplatform Early Predictors of Academic Articles’ Visibility and Citations
CI '25: Proceedings of the ACM Collective Intelligence Conference, August 2025
José Miguel Córdova Sánchez, Toma Hirose, Haohan Shi, Emőke-Ágnes Horvát, Daniel Romero
The dissemination of scientific articles across digital platforms has become a cornerstone of modern science communication. This study examines the dynamics of online attention to scientific content, analyzing over 530,000 research articles mentioned across platforms such as social media, blogs, and news outlets. We investigate the predictive power of temporal, platform, and paper features, in predicting both online visibility and citation count. Our findings reveal that early temporal features—such as the pace and timing of initial mentions—are the dominant predictors of an article’s eventual online mentions, pointing to the importance of collective dynamics around the online reception of research articles. Notably, the importance of predictive features differ across platforms, with temporal features dominating overall online visibility predictions, while news coverage prediction improves with a more balanced combination of paper and temporal features. Additionally, we show that online mentions of research articles, including their temporal distribution, can serve as early indicators of future citation counts. Overall, we demonstrate how early online signals can be leveraged to identify potentially influential articles. These insights highlight the value of considering how online mentions evolve over time, rather than focusing solely on their volume.
How Transparent is Usable Privacy and Security Research? A Meta-Study on Current Research Transparency Practices
34th USENIX Security Symposium, August 2025
Jan H. Klemmer, Juliane Schmüser, Fabian Fischer, Jacques Suray, Jan-Ulrich Holtgrave, Simon Lenau, Byron M. Lowens, Florian Schaub, Sascha Fahl
Transparent research reporting is crucial to understanding and assessing research, its results and validity, and for fostering replication. While other research fields investigated reporting and transparency practices, similar meta-research is missing for the usable privacy and security (UPS) community, which combines security, privacy, and human research. To gain insights into current research transparency practices and their development in the UPS community, we analyzed 200 UPS publications from twelve venues (including USENIX Security, IEEE S&P, CCS, SOUPS, and CHI) from 2018 to 2023. Additionally, we evaluated those venues' 81 calls for papers (CfPs) and 20 calls for artifacts (CfAs). We find that most papers report on many of 52 analyzed transparency criteria, but none achieve full transparency. Moreover, we uncover several areas that need improvements: essential artifacts like questionnaires are frequently missing and hinder replication, some information is reported inconsistently, and dead links further reduce availability. Our regression analysis indicates that paper length and the number of studies described in a paper impact reporting transparency, while we observed no effect of publication year and artifact evaluation (AE). Finally, we provide recommendations for authors, venues, and PC chairs to improve research transparency practices and suggest transparency guidelines.
Valuing curation infrastructures
JASIST, July 2025
Morgan F. Wofford, Andrea K. Thomer, Libby Hemphill, Katherine Polasek, Elizabeth Yakel
This study uses a theoretical lens of infrastructural dimensions to examine stakeholders' perceptions of the value of curation, focusing on the social science data repository, the Inter-university Consortium for Political and Social Research (ICPSR). Drawing on 67 interviews with both internal (ICPSR staff ) and external (funders, data producers, and reusers) stakeholders, we analyze how value is ascribed to curation across technical, organizational, and social components of infrastructure. We identify five key ways interviewees conceptualized the value of curation infrastructures: supporting sustainability and durability, enabling research efficiency, fostering trust, building community, and advancing data equity. Our findings highlight the role of curation in knowledge generation by reframing curation as infrastructure rather than a set of discrete practices. We clarify how transparency operates in dual—and sometimes conflicting—ways: as both understandability and invisibility, shaping trust in and access to data repositories. Second, we demonstrate how data equity is increasingly perceived by stakeholders as a core infrastructural value, enacted through practices that lower barriers to access. Finally, we surface the persistent challenges in evaluating and funding curation infrastructures due to their long time horizons and often-invisible nature. This work advocates recognizing and funding curation infrastructures as essential for long-term scientific and societal progress.
Coordinating Chaos: A Structured Review of Linguistic Coordination Methodologies
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics, July 2025
Benjamin Roger Litterer, David Jurgens, Dallas Card
Linguistic coordination—a phenomenon where conversation partners end up having similar patterns of language use—has been established across a variety of contexts and for multiple linguistic features. However, the study of language coordination has been accompanied by a diverse and inconsistently applied set of measures and theoretical perspectives. This diversity has significant consequences, as replication studies have highlighted the brittleness of certain measures and called influential findings into question. While prior work has addressed specific modeling decisions and model types, linguistic coordination research has yet to fully examine, synthesize, and critique the space of modeling choices available. In this work, we present a framework to organize the linguistic coordination literature. Using this schema, we provide a high-level overview of the choices involved in the measurement process and synthesize relevant critiques. Based on both gaps and limitations surfaced from this review, we suggest directions for further exploration and evaluation. In doing so, we provide the clarity required for linguistic coordination research to arrive at interpretable and sound conclusions.
The Million Authors Corpus: A Cross-Lingual and Cross-Domain Wikipedia Dataset for Authorship Verification
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics, July 2025
Abraham Israeli, Shuai Liu, Jonathan May, David Jurgens
Authorship verification (AV) is a crucial task for applications like identity verification, plagiarism detection, and AI-generated text identification. However, datasets for training and evaluating AV models are primarily in English and primarily in a single domain. This precludes analysis of AV techniques for generalizability and can cause seemingly valid AV solutions to, in fact, rely on topic-based features rather than actual authorship features. To address this limitation, we introduce the Million Authors Corpus (MAC), a novel dataset encompassing contributions from dozens of languages on Wikipedia. It includes only long and contiguous textual chunks taken from Wikipedia edits and links those texts to their authors. MAC includes 60.08M textual chunks, contributed by 1.29M Wikipedia authors. It enables broad-scale cross-lingual and crossdomain AV evaluation to ensure accurate analysis of model capabilities that are not overly optimistic. We provide baseline evaluations using state-of-the-art AV models as well as information retrieval models that are not AV-specific, in order to demonstrate MAC’s unique crosslingual and cross-domain ablation capabilities.
Additionally, the associations between team trust, communication, and performance were investigated.
Trust in the Team as a Function of Trust in Individual Agents: Scale Validation and Modeling
Proceedings of the Human Factors and Ergonomics Society Annual Meeting, July 2025
Hyesun Chung, X. Jessie Yang
With the growing adoption of autonomous technologies across various domains, an increasing number of studies have explored collaborations between humans and agents working together to achieve shared goals, forming human-agent teams (HATs). While much of the research has focused on dyadic relationships involving a single human and a single agent, the current study examines multi-human multi-agent teams where multiple humans and agents collaborate to achieve team goals. The shift from dyadic to more complex teams makes research topics studied in triadic or larger human-human teams relevant to HATs. Of particular interest in this study is the concept of “trust in team” and its relationships with trustor and trustee characteristics. The study used an adapted version of the Blocks World for Teams (BW4T) testbed, where a team of four (two humans and two agents) performs a collaborative block-moving task. This study first validated the use of the existing interpersonal trust scale and team trust scale for evaluating trust in human/agent teammates and in the team, respectively. The next step involved examining how trust in the team is formed in relation to trust in individual teammates. Additionally, the associations between team trust, communication, and performance were investigated.
Promoting caste equality in the labor market: The role of self-confidence
PLOS One, July 2025
Qiqi Wang, Tushi Baul, Sujoy Chakravarty, Tanya Rosenblat
We study how people perceive the self-confidence of individuals from different castes in India. In an experimental Indian labor market where employers and workers belong to different castes, employers evaluate worker resumes to predict the future productivity of workers who perform a real effort task. The baseline group uses resumes that reveal a productivity signal i.e.- performance in a practice task and caste information, while the treatment group receives an additional measure of worker self-confidence. We find that employers in both groups exhibit a discriminatory wage differential against lower caste workers. However, employers in the treatment group weigh lower caste workers’ self-confidence more heavily than that of higher caste workers. This differential effect of confidence compensates for the lower evaluation and hence wage given to lower caste workers due to discrimination. From a policy perspective, these findings highlight the importance of non-cognitive skill training, such as training sessions for employment interviews where applicants can signal their self-confidence through interaction with employers.
Mapping the Podcast Ecosystem with the Structured Podcast Research Corpus
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics, July 2025
Benjamin Roger Litterer, David Jurgens, Dallas Card
Podcasts provide highly diverse content to a massive listener base through a unique on-demand modality. However, limited data has prevented large-scale computational analysis of the podcast ecosystem. To fill this gap, we introduce a massive dataset of over 1.1M podcast transcripts that is largely comprehensive of all English language podcasts available through public RSS feeds from May and June of 2020. This data is not limited to text, but includes metadata, inferred speaker roles, and audio features and speaker turns for a subset of 370K episodes. Using this data, we conduct a foundational investigation into the content, structure, and responsiveness of this ecosystem. Together, our data and analyses open the door to continued computational research of this popular and impactful medium.
Meaning Variation and Data Quality in the Corpus of Founding Era American English
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics, July 2025
Legal scholars are increasingly using corpus based methods for assessing historical meaning. Among work focused on the so-called founding era (mid to late 18th century), the majority of such studies use the Corpus of Founding Era American English (COFEA) and rely on methods such as word counting and manual coding. Here, we demonstrate what can be inferred about meaning change and variation using more advanced NLP methods, focusing on terms in the U.S. Constitution. We also carry out a data quality assessment of COFEA, pointing out issues with OCR quality and metadata, compare diachronic change to synchronic variation, and discuss limitations when using NLP methods for studying historical meaning.
Semantic change in adults is not primarily a generational phenomenon
Proceedings of the National Academy of Sciences, June 2025
Gaurav Kamath, Michelle Yang, Siva Reddy, Morgan Sonderegger, Dallas Card
A central question in the study of language change is whether or not such change is generational. If a language changes over time generation-by-generation, the process looks as follows: New generations of speakers introduce innovations, while older speakers conserve their usage patterns, and the language changes as new generations replace older ones. At the opposite extreme, language change could be a zeitgeist phenomenon, in which changes are universally adopted by speakers simultaneously, regardless of age or generational cohort. This paper asks this question in the context of word meaning change. We analyze meaning change in over 100 words across more than 7.9 million U.S. congressional speeches, to observe whether, when a word sense rises or falls in prominence, adult speakers from different generations uniformly adopt it, or those from older generations conserve their prior usage. Using language model-based word sense induction methods, we identify different senses of each word, and then model the prevalence of each of these word senses as a function of time and speaker age. We find that most words show a small but statistically significant effect of speaker age; across almost 140 y of Congress, older speakers typically take longer than younger speakers to follow changes in word usage, but nevertheless do so within a few years. Our findings indicate that despite minor age-based differences, word meaning change among mature speakers is likely not a generational process, but rather a zeitgeist process, in which older adult speakers can readily adopt new word usage patterns.
Pre-prints, Working Papers, Articles, Workshops and Talks
Perceiving Slope and Acceleration: Evidence for Variable Tempo Sampling in Pitch-Based Sonification of Functions
arXiv, August 2025
Danyang Fan, Walker Smith, Takako Fujioka, Chris Chafe, Sile O’ Modhrain, Diana Deutsch, Sean Follmer
Sonification offers a non-visual way to understand data, with pitch-based encodings being the most common. Yet, how well people perceive slope and acceleration-key features of data trends-remains poorly understood. Drawing on people's natural abilities to perceive tempo, we introduce a novel sampling method for pitch-based sonification to enhance the perception of slope and acceleration in univariate functions. While traditional sonification methods often sample data at uniform x-spacing, yielding notes played at a fixed tempo with variable pitch intervals (Variable Pitch Interval), our approach samples at uniform y-spacing, producing notes with consistent pitch intervals but variable tempo (Variable Tempo). We conducted psychoacoustic experiments to understand slope and acceleration perception across three sampling methods: Variable Pitch Interval, Variable Tempo, and a Continuous (no sampling) baseline. In slope comparison tasks, Variable Tempo was more accurate than the other methods when modulated by the magnitude ratio between slopes. For acceleration perception, just-noticeable differences under Variable Tempo were over 13 times finer than with other methods. Participants also commonly reported higher confidence, lower mental effort, and a stronger preference for Variable Tempo compared to other methods. This work contributes models of slope and acceleration perception across pitch-based sonification techniques, introduces Variable Tempo as a novel and preferred sampling method, and provides promising initial evidence that leveraging timing can lead to more sensitive, accurate, and precise interpretation of derivative-based data features.
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