arXiv subjects

Yong-Yeol Ahn

Publications and source records attributed to Yong-Yeol Ahn.

At least 19 recordsLinked to original sources

arXiv2026

Dataset repurposing and disruptive AI research

Technological advancements are enabling increasingly systematic and large-scale data collection across all areas of science, driving scientific innovation. In particular, AI research exemplifies this trend, having advanced rapidly through the assembly of massive datasets used to train and evaluate machine learning models. However, the escalating demand for data, the difficulty of creating high-quality datasets, and the exhaustion of easily accessible data sources in AI research raise important questions about how to maximize the value of existing datasets through recombination and repurposing. Here, we draw on two theoretical frameworks---recombinational novelty and transformational creativity---to examine the practice of data repurposing and its scientific impact. Focusing on AI, we analyze scientific outcomes associated with data repurposing across more than 10,000 machine learning papers. First, we find that although most repurposed datasets do not achieve broad visibility in the short term, data repurposing is associated with greater disruption. Second, when repurposed data is adopted by subsequent research, the repurposing paper is associated with higher disruption and increased citation impact. Third, repurposing teams tend to be more experienced, more institutionally prestigious, and involve academic--industry collaboration. However, team characteristics poorly predict which repurposed datasets will be adopted by the community. These findings suggest that data repurposing may be an important approach to scientific discovery, and that its successful adoption is more common among larger teams and collaborations spanning academia and industry.

cs.CY↗

arXiv2026

Federating Governance: How Community Rules Scale with Mastodon Instances

The rise of decentralized social media platforms like Mastodon and Bluesky highlights the challenge of scaling self-governance and moderation. As communities grow, they face new issues that demand increasingly complex governance structures. However, as moderation is mainly volunteer-driven, there is limited formal guidance on how community rules and moderation practices should evolve with growth. This study investigates how moderation scale with Mastodon instances by analyzing community rules across servers of varying sizes. We categorize these rules to identify key governance priorities and find that these priorities are remarkably consistent across instance sizes: rules addressing problematic content, such as harassment, hate speech, and illegal content, dominate regardless of scale. While smaller communities focus on narrower sets of topics, larger servers maintain a more balanced coverage of a broad range of topics. Our analysis of rule formalization reveals that community size strongly predicts rule development. As instances grow, their rules become more extensive and topically diverse, but also exhibit lower readability and linguistic diversity. In contrast, external federation interactions have a limited role, mainly associated with a broader scope of rules without substantially affecting their diversity or form. These findings highlight the relative influence of internal versus external factors, suggesting that local scaling pressures outweigh network-level dynamics in decentralized social media governance. The scaling pattern observed on Mastodon resemble those previously identified on centralized platforms such as Reddit, suggesting that community size imposes fundamental constraints on self-governance that transcend platform architectures

cs.SI↗

arXiv2026

Emergence of Stereotypes and Affective Polarization from Belief Network Dynamics

Our belief systems are shaped by social processes, such as observations and influence, and by cognitive processes, such as the drive for internal coherence. These processes steer how individual beliefs evolve and become connected. The resulting belief networks contain both causal and associative links, including spurious ones, such as stereotypes. Here, we develop an agent-based model of belief networks that demonstrates how two basic mechanisms -- social interaction and a drive for internal coherence -- can give rise to such stereotypes without any underlying reality. We further demonstrate how stereotypes, when coupled with shared group identity, can give rise to affective polarization, even in the absence of ideological conflicts.

cs.SI↗

arXiv2026

LLMs Can Infer Political Alignment from Online Conversations

Due to the correlational structure in our traits such as identities, cultures, and political attitudes, seemingly innocuous preferences like following a band or using a specific slang can reveal private traits. This possibility, especially when combined with massive, public social data and advanced computational methods, poses a fundamental privacy risk. As our data exposure online and the rapid advancement of AI are increasing the risk of misuse, it is critical to understand the capacity of large language models (LLMs) to exploit such potential. Here, using online discussions on DebateOrg and Reddit, we show that LLMs can reliably infer hidden political alignment, significantly outperforming traditional machine learning models. Prediction accuracy further improves as we aggregate multiple text-level inferences into a user-level prediction, and as we use more politics-adjacent domains. We demonstrate that LLMs leverage words that are highly predictive of political alignment while not being explicitly political. Our findings underscore the capacity and risks of LLMs for exploiting socio-cultural correlates.

cs.SI↗

arXiv2026

What Helps Language Models Predict Human Beliefs: Demographics or Prior Stances?

Beliefs shape how people reason, communicate, and behave. Rather than existing in isolation, they exhibit a rich correlational structure--some connected through logical dependencies, others through indirect associations or social processes. As usage of large language models (LLMs) becomes more ubiquitous in our society, LLMs' ability to understand and reason through human beliefs has many implications from privacy issues to personalized persuasion and the potential for stereotyping. Yet how LLMs capture this interrelated landscape of beliefs remains unclear. For instance, when predicting someone's beliefs, what information affects the prediction most--who they are (demographics), what else they believe (prior stances), or a combination of both? We address these questions using data from an online debate platform, evaluating the ability of off-the-shelf open-weight LLMs to predict individuals' stance under four conditions: no context, demographics only, prior beliefs only, and both combined. We find that both types of information improve predictions over a blind baseline, with their combination yielding the best performance in most cases. However, the relative value of each varies substantially across belief domains. These findings reveal how current LLMs leverage different types of social information when reasoning about human beliefs, highlighting both their capabilities and limitations.

cs.CL↗

arXiv2025

Adaptive cut reveals multiscale complexity in networks

Hierarchical clustering and community detection are important problems in machine learning and complex network analysis. A common approach to identify clusters is to simply cut dendrograms at some threshold. However, single-level cuts are often suboptimal in terms of capturing underlying structure in the data, especially when the dendrogram is unbalanced. In this paper, we present the adaptive cut, a novel method that leverages the hierarchical structure of dendrograms by employing multi-level cuts to overcome the limitations of single-level approaches. The adaptive cut optimizes an objective function using a Markov chain Monte Carlo with simulated annealing, resulting in better partitions. We demonstrate the effectiveness of the adaptive cut through applications to link clustering and modularity optimization, but note that the method is applicable to any clustering task that relies on a dendrogram and an objective function. Beyond the adaptive cut, we introduce the balancedness score, an information-theoretic metric that quantifies how balanced a dendrogram is. Balancedness predicts the potential benefits of using multi-level cuts. For the community detection examples, we evaluate our method on more than 200 real-world networks and multiple synthetic datasets, demonstrating significant improvements in partition density and modularity over traditional single-cut approaches. In addition, we show the generality of the adaptive cut by applying it across various hierarchical clustering techniques and objective functions. Our results indicate that the adaptive cut provides a robust and versatile tool for improving clustering outcomes.

physics.soc-ph↗

arXiv2025

One pathogen does not an epidemic make: A review of interacting contagions, diseases, beliefs, and stories

From pathogens and computer viruses to genes and memes, contagion models have found widespread utility across the natural and social sciences. Despite their success and breadth of adoption, the approach and structure of these models remain surprisingly siloed by field. Given the siloed nature of their development and widespread use, one persistent assumption is that a given contagion can be studied in isolation, independently from what else might be spreading in the population. In reality, countless contagions of biological and social nature interact within hosts (interacting with existing beliefs, or the immune system) and across hosts (interacting in the environment, or affecting transmission mechanisms). Additionally, from a modeling perspective, we know that relaxing these assumptions has profound effects on the physics and translational implications of the models. Here, we review mechanisms for interactions in social and biological contagions, as well as the models and frameworks developed to include these interactions in the study of the contagions. We highlight existing problems related to the inference of interactions and to the scalability of mathematical models and identify promising avenues of future inquiries. In doing so, we highlight the need for interdisciplinary efforts under a unified science of contagions and for removing a common dichotomy between social and biological contagions.

physics.soc-ph↗

arXiv2025

Cognitive Linguistic Identity Fusion Score (CLIFS): A Scalable Cognition-Informed Approach to Quantifying Identity Fusion from Text

Quantifying identity fusion -- the psychological merging of self with another entity or abstract target (e.g., a religious group, political party, ideology, value, brand, belief, etc.) -- is vital for understanding a wide range of group-based human behaviors. We introduce the Cognitive Linguistic Identity Fusion Score (CLIFS), a novel metric that integrates cognitive linguistics with large language models (LLMs), which builds on implicit metaphor detection. Unlike traditional pictorial and verbal scales, which require controlled surveys or direct field contact, CLIFS delivers fully automated, scalable assessments while maintaining strong alignment with the established verbal measure. In benchmarks, CLIFS outperforms both existing automated approaches and human annotation. As a proof of concept, we apply CLIFS to violence risk assessment to demonstrate that it can improve violence risk assessment by more than 240%. Building on our identification of a new NLP task and early success, we underscore the need to develop larger, more diverse datasets that encompass additional fusion-target domains and cultural backgrounds to enhance generalizability and further advance this emerging area. CLIFS models and code are public at https://github.com/DevinW-sudo/CLIFS.

cs.CL↗

arXiv2025

The Potential Impact of Disruptive AI Innovations on U.S. Occupations

The rapid rise of AI is poised to disrupt the labor market. However, AI is not a monolith; its impact depends on both the nature of the innovation and the jobs it affects. While computational approaches are emerging, there is no consensus on how to systematically measure an innovation's disruptive potential. Here, we calculate the disruption index of 3,237 U.S. AI patents (2015-2022) and link them to job tasks to distinguish between "consolidating" AI innovations that reinforce existing structures and "disruptive" AI innovations that alter them. Our analysis reveals that consolidating AI primarily targets physical, routine, and solo tasks, common in manufacturing and construction in the Midwest and central states. By contrast, disruptive AI affects unpredictable and mental tasks, particularly in coastal science and technology sectors. Surprisingly, we also find that disruptive AI disproportionately affects areas already facing skilled labor shortages, suggesting disruptive AI technologies may accelerate change where workers are scarce rather than replacing a surplus. Ultimately, consolidating AI appears to extend current automation trends, while disruptive AI is set to transform complex mental work, with a notable exception for collaborative tasks.

cs.CY↗

arXiv2025

Beyond Distance: Mobility Neural Embeddings Reveal Visible and Invisible Barriers in Urban Space

Human mobility in cities is shaped not only by visible structures such as highways, rivers, and parks but also by invisible barriers rooted in socioeconomic segregation, uneven access to amenities, and administrative divisions. Yet identifying and quantifying these barriers at scale and their relative importance on people's movements remains a major challenge. Neural embedding models, originally developed for language, offer a powerful way to capture the complexity of human mobility from large-scale data. Here, we apply this approach to 25.4 million observed trajectories across 11 major U.S. cities, learning mobility embeddings that reveal how people move through urban space. These mobility embeddings define a functional distance between places, one that reflects behavioral rather than physical proximity, and allow us to detect barriers between neighborhoods that are geographically close but behaviorally disconnected. We find that the strongest predictors of these barriers are differences in access to amenities, administrative borders, and residential segregation by income and race. These invisible borders are concentrated in urban cores and persist across cities, spatial scales, and time periods. Physical infrastructure, such as highways and parks, plays a secondary but still significant role, especially at short distances. We also find that individuals who cross barriers tend to do so outside of traditional commuting hours and are more likely to live in areas with greater racial diversity, and higher transit use or income. Together, these findings reveal how spatial, social, and behavioral forces structure urban accessibility and provide a scalable framework to detect and monitor barriers in cities, with applications in planning, policy evaluation, and equity analysis.

cs.CY↗

arXiv2025

A semantic embedding space based on large language models for modelling human beliefs

Beliefs form the foundation of human cognition and decision-making, guiding our actions and social connections. A model encapsulating beliefs and their interrelationships is crucial for understanding their influence on our actions. However, research on belief interplay has often been limited to beliefs related to specific issues and relied heavily on surveys. We propose a method to study the nuanced interplay between thousands of beliefs by leveraging an online user debate data and mapping beliefs onto a neural embedding space constructed using a fine-tuned large language model (LLM). This belief space captures the interconnectedness and polarization of diverse beliefs across social issues. Our findings show that positions within this belief space predict new beliefs of individuals and estimate cognitive dissonance based on the distance between existing and new beliefs. This study demonstrates how LLMs, combined with collective online records of human beliefs, can offer insights into the fundamental principles that govern human belief formation.

cs.CL↗

arXiv2025

Uncovering simultaneous breakthroughs with a robust measure of disruptiveness

Progress in science and technology is punctuated by disruptive innovation and breakthroughs. Researchers have characterized these disruptions to explore the factors that spark such innovations and to assess their long-term trends. However, although understanding disruptive breakthroughs and their drivers hinges upon accurately quantifying disruptiveness, the core metric used in previous studies -- the disruption index -- remains insufficiently understood and tested. Here, after demonstrating the critical shortcomings of the disruption index, including its conflicting evaluations for simultaneous discoveries, we propose a new, continuous measure of disruptiveness based on a neural embedding framework that addresses these limitations. Our measure not only better distinguishes disruptive works, such as Nobel Prize-winning papers, from others, but also reveals simultaneous disruptions by allowing us to identify the "twins" that have the most similar future context. By offering a more robust and precise lens for identifying disruptive innovations and simultaneous discoveries, our study provides a foundation for deepening insights into the mechanisms driving scientific breakthroughs while establishing a more equitable basis for evaluating transformative contributions.

cs.SI↗

arXiv2025

Social inequality and cultural factors impact the awareness and reaction during the cryptic transmission period of pandemic

The World Health Organization (WHO) declared the COVID-19 outbreak a Public Health Emergency of International Concern (PHEIC) on January 31, 2020. However, rumors of a "mysterious virus" had already been circulating in China in December 2019, possibly preceding the first confirmed COVID-19 case. Understanding how awareness about an emerging pandemic spreads through society is vital not only for enhancing disease surveillance, but also for mitigating demand shocks and social inequities, such as shortages of personal protective equipment (PPE) and essential supplies. Here we leverage a massive e-commerce dataset comprising 150 billion online queries and purchase records from 94 million people to detect the traces of early awareness and public response during the cryptic transmission period of COVID-19. Our analysis focuses on identifying information gaps across different demographic cohorts, revealing significant social inequities and the role of cultural factors in shaping awareness diffusion and response behaviors. By modeling awareness diffusion in heterogeneous social networks and analyzing online shopping behavior, we uncover the evolving characteristics of vulnerable populations. Our findings expand the theoretical understanding of awareness spread and social inequality in the early stages of a pandemic, highlighting the critical importance of e-commerce data and social network data in effectively and timely addressing future pandemic challenges. We also provide actionable recommendations to better manage and mitigate dynamic social inequalities in public health crises.

cs.SI↗

arXiv2025

Representing the Disciplinary Structure of Physics: A Comparative Evaluation of Graph and Text Embedding Methods

Recent advances in machine learning offer new ways to represent and study scholarly works and the space of knowledge. Graph and text embeddings provide a convenient vector representation of scholarly works based on citations and text. Yet, it is unclear whether their representations are consistent or provide different views of the structure of science. Here, we compare graph and text embedding by testing their ability to capture the hierarchical structure of the Physics and Astronomy Classification Scheme (PACS) of papers published by the American Physical Society (APS). We also provide a qualitative comparison of the overall structure of the graph and text embeddings for reference. We find that neural network-based methods outperform traditional methods and graph embedding methods such as node2vec are better than other methods at capturing the PACS structure. Our results call for further investigations into how different contexts of scientific papers are captured by different methods, and how we can combine and leverage such information in an interpretable manner.

cs.SI↗

arXiv2025

Modeling the amplification of epidemic spread by individuals exposed to misinformation on social media

Understanding how misinformation affects the spread of disease is crucial for public health, especially given recent research indicating that misinformation can increase vaccine hesitancy and discourage vaccine uptake. However, it is difficult to investigate the interaction between misinformation and epidemic outcomes due to the dearth of data-informed holistic epidemic models. Here, we employ an epidemic model that incorporates a large, mobility-informed physical contact network as well as the distribution of misinformed individuals across counties derived from social media data. The model allows us to simulate various scenarios to understand how epidemic spreading can be affected by misinformation spreading through one particular social media platform. Using this model, we compare a worst-case scenario, in which individuals become misinformed after a single exposure to low-credibility content, to a best-case scenario where the population is highly resilient to misinformation. We estimate the additional portion of the U.S. population that would become infected over the course of the COVID-19 epidemic in the worst-case scenario. This work can provide policymakers with insights about the potential harms of exposure to online vaccine misinformation.

cs.SI↗

arXiv2025

Community-centric modeling of citation dynamics explains collective citation patterns in science, law, and patents

Many human knowledge systems, such as science, law, and invention, are built on documents and the citations that link them. Citations, while serving multiple purposes, primarily function as a way to explicitly document the use of prior work and thus have become central to the study of knowledge systems. Analyzing citation dynamics has revealed statistical patterns that shed light on knowledge production, recognition, and formalization, and has helped identify key mechanisms driving these patterns. However, most quantitative findings are confined to scientific citations, raising the question of universality of these findings. Moreover, existing models of individual citation trajectories fail to explain phenomena such as delayed recognition, calling for a unifying framework. Here, we analyze a newly available corpus of U.S. case law, in addition to scientific and patent citation networks, to show that they share remarkably similar citation patterns, including a heavy-tailed distribution of sleeping beauties. We propose a holistic model that captures the three core mechanisms driving collective dynamics and replicates the elusive phenomenon of delayed recognition. We demonstrate that the model not only replicates observed citation patterns, but also better predicts future successes by considering the whole system. Our work offers insights into key mechanisms that govern large-scale patterns of collective human knowledge systems and may provide generalizable perspectives on discovery and innovation across domains.

physics.soc-ph↗

arXiv2024

Network community detection via neural embeddings

Recent advances in machine learning research have produced powerful neural graph embedding methods, which learn useful, low-dimensional vector representations of network data. These neural methods for graph embedding excel in graph machine learning tasks and are now widely adopted. However, how and why these methods work -- particularly how network structure gets encoded in the embedding -- remain largely unexplained. Here, we show that node2vec -- shallow, linear neural network -- encodes communities into separable clusters better than random partitioning down to the information-theoretic detectability limit for the stochastic block models. We show that this is due to the equivalence between the embedding learned by node2vec and the spectral embedding via the eigenvectors of the symmetric normalized Laplacian matrix. Numerical simulations demonstrate that node2vec is capable of learning communities on sparse graphs generated by the stochastic blockmodel, as well as on sparse degree-heterogeneous networks. Our results highlight the features of graph neural networks that enable them to separate communities in embedding space.

physics.soc-ph↗

arXiv2024

Persistent Hierarchy in Contemporary International Collaboration

Science is increasingly global, with international collaboration playing a crucial role in advancing scientific development and knowledge exchange across borders. However, the processes that regulate how scientific labor is distributed among countries remain underexplored, leading to challenges in ensuring both effective collaboration and equitable participation across diverse scientific communities. Here, we leverage three million internationally coauthored publications produced by countries worldwide to examine the division of scientific labor in international collaboration, identify the factors that shape this distribution, and assess its broader consequences. Our findings uncover a persistent hierarchical structure in international collaboration, with researchers from scientifically advanced countries tend to occupy leading roles, while those from less-developed countries are often relegated to supportive roles, even after controlling for various influential factors. This hierarchy is also reflected in the research content, as countries with lower scientific capacity tend to participate in international collaborations that deviate from their domestic science. By analyzing the labor division within international collaborations, we demonstrate that researchers from less-developed countries face systematic disadvantages, which not only limit their contributions to the global scientific community but also prevent them from fully benefiting from international collaborations.

cs.DL↗