arXiv subjects

Akira Matsui

Publications and source records attributed to Akira Matsui.

At least 19 recordsLinked to original sources

arXiv2026

Return of the solo author: The changing division of labor in science in the age of generative AI

Modern science has experienced a long shift from individual work to team production. Generative artificial intelligence (AI) might appear to extend this trajectory by lowering research costs and enabling larger-scale collaboration. Yet if tasks once performed by coauthors can be delegated to AI, the same technology may also weaken the need for collaboration in parts of the research process. Here, we examine this tension by moving beyond average team size and focusing on the solo-authored tail of the author-count distribution. Analyzing over 300 million works across 26 fields, we find that the decades-long decline in solo authorship halted and partially reversed with ChatGPT's public release in late 2022. We also reveal that this is an uneven phenomenon: it is strongest in fields where coauthors' work is more readily replaceable, and weak or absent in fields that depend on physical collaboration. At the individual level, the recovery is not explained by the entry of new researchers or by changes in field composition. Instead, the break appears among authors who had written only with others, including those with no prior solo publications, and among long-established authors as well as newcomers. Their solo papers stay close to their own coauthored work while narrowing in scope and shifting toward computational topics. Because a solo paper is work without credited human coauthors, this study offers an empirical probe of how generative AI can substitute for scientific labor, and evidence of a reconfiguration of cognitive labor within papers rather than of team size.

cs.CY↗

arXiv2025

Gendered Cultural Discourse in Japan across the Prewar-Postwar Transition: Evidence from Historical Word Embeddings

We quantify the evolution of gender stereotypes in Japan from 1900 to 1998, covering the prewar-postwar transition, using a series of yearly word embeddings trained on historical text corpora. We define the gender stereotype value to measure the strength of a word's gender association by computing the difference in cosine similarity of the word to female- versus male-related attribute words. We examine trajectories of gender stereotype across three traditionally gendered domains: Home, Work, and Politics. To provide a more granular analysis and strengthen the robustness of our findings in the Work domain, we also examine changes in gender stereotypes across 18 occupations and calculate their correlations with gender participation statistics. Our results reveal domain-specific patterns. In the Home domain, female stereotype values remain stable over time, showing no statistically significant changes. In contrast, the Work and Politics domains exhibit significant trend reversals in female stereotype values around 1945, shifting from negative prewar trends to positive postwar trends, indicating increasing female associations in public domains over time. These findings show that trends in gendered cultural discourse shifted unevenly around 1945: the observed changes in the Work and Politics domains are temporally aligned with postwar institutional transformations, including reforms under the U.S.-led Allied Occupation, while the Home domain shows more limited change. Furthermore, female stereotype values for occupations show a moderately positive correlation (r=0.364) with the proportion of women in each occupation, indicating that word-embedding-based measures of gender stereotype mirrored demographic shifts to a meaningful extent.

cs.CY↗

arXiv2025

Modeling User Redemption Behavior in Complex Incentive Digital Environment: An Empirical Study Using Large-Scale Transactional Data

The digital economy implements complex incentive systems to retain users through point redemption. Understanding user behavior in such complex incentive structures presents a fundamental challenge, especially in estimating the value of these digital assets against traditional money. This study tackles this question by analyzing large-scale, real-world transaction data from a popular personal finance application that captures both monetary spending and point-based transactions. We find that point usage is linked to demographics. Our analysis using a natural experiment and a causal inference technique reveals that a large point grant stimulated an increase in point spending without a detectable effect on cash expenditure. We then find an association between consumers' shopping styles and their point redemption patterns. This study, on a massive real-world economic ecosystem, examines how consumers behave in multi-currency environments, with direct implications for modeling economic behavior and designing digital platforms.

cs.CY↗

arXiv2025

Data-driven Methods of Extracting Text Structure and Information Transfer

The Anna Karenina Principle (AKP) holds that success requires satisfying a small set of essential conditions, whereas failure takes diverse forms. We test AKP, its reverse, and two further patterns described as ordered and noisy across novels, online encyclopedias, research papers, and movies. Texts are represented as sequences of functional blocks, and convergence is assessed in transition order and position. Results show that structural principles vary by medium: novels follow reverse AKP in order, Wikipedia combines AKP with ordered patterns, academic papers display reverse AKP in order but remain noisy in position, and movies diverge by genre. Success therefore depends on structural constraints that are specific to each medium, while failure assumes different shapes across domains.

cs.AI↗

arXiv2025

User Exploration and Exploitation Behavior Under the Influence of Real-time Interactions in Live Streaming Environments

Live streaming platforms offer a distinctive way for users and content creators to interact with each other through real-time communication. While research on user behavior in online platforms has explored how users discover their favorite content from creators and engage with them, the role of real-time features remains unclear. There are open questions as to what commonalities and differences exist in users' relationships with live streaming platforms compared to traditional on-demand style platforms. To understand this, we employ the concept of Exploration/Exploitation (E/E) and analyze a large-scale dataset from a live streaming platform over two years. Our results indicate that even on live streaming platforms, users exhibit E/E behavior but experience a longer exploration period. We also identify external factors, such as circadian rhythms, that influence E/E dynamics and user loyalty. The presented study emphasizes the importance of balancing E/E in online platform design, especially for live streaming platforms, providing implications that suggest design strategies for platform developers and content creators to facilitate timely engagement and retention.

cs.HC↗

arXiv2025

Global Patterns of Knowledge: Language, Genre, and the Geography of Knowledge

Online platforms, particularly Wikipedia, have become critical infrastructures for providing diverse linguistic and cultural contexts. This human-curated knowledge now forms the foundation for modern AI. However, we have not yet fully explored how knowledge production capability vary across languages and domains. Here, we address this gap by applying economic complexity analysis to understand the editing history of Wikipedia platforms. This approach allows us to infer the latent mode of ``knowledge-production'' of each language community from the diversity and specialization of its contributed content. We reveal that different language communities exhibit distinct specializations, particularly in cultural subjects. Furthermore, we map the global landscape of these production modes, finding that the structure of knowledge production strongly reflects geopolitical boundaries. Our findings suggest that while a common mode of knowledge production exists for standardized topics such as science, it is more diverse for cultural topics or controversial subjects such as conspiracy theories. The association between differences in knowledge production capability and geopolitical factors implies how linguistic and cultural dynamics shape our worldview and the biases embedded in Wikipedia data, a unique, massive, and essential dataset for modern AI.

cs.CY↗

arXiv2025

Disruptive Transformation of Artworks in Master-Disciple Relationships: The Case of Ukiyo-e Artworks

Artwork research has long relied on human sensibility and subjective judgment, but recent developments in machine learning have enabled the quantitative assessment of features that humans could not discover. In Western paintings, comprehensive analyses have been conducted from various perspectives in conjunction with large databases, but such extensive analysis has not been sufficiently conducted for Eastern paintings. Then, we focus on Ukiyo-e, a traditional Japanese art form, as a case study of Eastern paintings, and conduct a quantitative analysis of creativity in works of art using 11,000 high-resolution images. This involves using the concept of calculating creativity from networks to analyze both the creativity of the artwork and that of the artists. As a result, In terms of Ukiyo-e as a whole, it was found that the creativity of its appearance has declined with the maturation of culture, but in terms of style, it has become more segmented with the maturation of culture and has maintained a high level of creativity. This not only provides new insights into the study of Ukiyo-e but also shows how Ukiyo-e has evolved within the ongoing cultural history, playing a culturally significant role in the analysis of Eastern art.

cs.CV↗

arXiv2025

The "recognition," "belief," and "action" regarding conspiracy theories: An empirical study using large-scale samples from Japan and the United States

Conspiracy theories present significant societal challenges, shaping political behavior, eroding public trust, and disrupting social cohesion. Addressing their impact requires recognizing that conspiracy engagement is not a singular act but a multi-stage process involving distinct cognitive and behavioral transitions. In this study, we investigate this sequential progression, "recognition," "belief," and "action" (demonstrative action and diffusion action), using nationally representative surveys from the United States (N=13,578) and Japan (N=16,693). Applying a Bayesian hierarchical model, we identify the key social, political, and economic factors that drive engagement at each stage, providing a structured framework for understanding the mechanisms underlying conspiracy theory adoption and dissemination. We find that recognition serves as a crucial gateway determining who transitions to belief, and that demonstrative and diffusion actions are shaped by distinct factors. Demonstrative actions are more prevalent among younger, higher-status individuals with strong political alignments, whereas diffusion actions occur across broader demographics, particularly among those engaged with diverse media channels. Our findings further reveal that early-life economic and cultural capital significantly influence the shape of conspiratorial engagement, emphasizing the role of life-course experiences. These insights highlight the necessity of distinguishing between different forms of conspiracy engagement and highlight the importance of targeted interventions that account for structural, cultural, and psychological factors to mitigate their spread and societal impact.

cs.CY↗

arXiv2024

Driving force of atomic ordering in Fe$_{1-x}$Pt$_{x}$, investigated by density functional theory and machine-learning interatomic potentials Monte Carlo simulations

We report the mechanisms of atomic ordering in Fe$_{1-x}$Pt$_{x}$ alloys using density functional theory (DFT) and machine-learning interatomic potential Monte Carlo (MLIP-MC) simulations. We clarified that the formation enthalpy of the ordered phase was significantly enhanced by spin polarization compared to that of the disordered phase. Analysis of the density of states indicated that coherence in local potentials in the ordered phase brings energy gain over the disordered phases, when spin is considered. MLIP-MC simulations were performed to investigate the phase transition of atomic ordering at a finite temperature. The model trained using the DFT dataset with spin polarization exhibited quantitatively good agreement with previous experiments and thermodynamic calculations across a wide range of Pt compositions, whereas the model without spin significantly underestimated the transition temperature. Through this study, we clarified that spin polarization is essential for accurately accounting for the ordered phase in Fe-Pt bimetallic alloys, even above the Curie temperature, possibly because of the remaining short-range spin order.

cond-mat.mtrl-sci↗

arXiv2024

Hybrid Forecasting of Geopolitical Events

Sound decision-making relies on accurate prediction for tangible outcomes ranging from military conflict to disease outbreaks. To improve crowdsourced forecasting accuracy, we developed SAGE, a hybrid forecasting system that combines human and machine generated forecasts. The system provides a platform where users can interact with machine models and thus anchor their judgments on an objective benchmark. The system also aggregates human and machine forecasts weighting both for propinquity and based on assessed skill while adjusting for overconfidence. We present results from the Hybrid Forecasting Competition (HFC) - larger than comparable forecasting tournaments - including 1085 users forecasting 398 real-world forecasting problems over eight months. Our main result is that the hybrid system generated more accurate forecasts compared to a human-only baseline which had no machine generated predictions. We found that skilled forecasters who had access to machine-generated forecasts outperformed those who only viewed historical data. We also demonstrated the inclusion of machine-generated forecasts in our aggregation algorithms improved performance, both in terms of accuracy and scalability. This suggests that hybrid forecasting systems, which potentially require fewer human resources, can be a viable approach for maintaining a competitive level of accuracy over a larger number of forecasting questions.

cs.CY↗

arXiv2023

The Chance of Winning Election Impacts on Social Media Strategy

Social media has been a paramount arena for election campaigns for political actors. While many studies have been paying attention to the political campaigns related to partisanship, politicians also can conduct different campaigns according to their chances of winning. Leading candidates, for example, do not behave the same as fringe candidates in their elections, and vice versa. We, however, know little about this difference in social media political campaign strategies according to their odds in elections. We tackle this problem by analyzing candidates' tweets in terms of users, topics, and sentiment of replies. Our study finds that, as their chances of winning increase, candidates narrow the targets they communicate with, from people in general to the electrical districts and specific persons (verified accounts or accounts with many followers). Our study brings new insights into the candidates' campaign strategies through the analysis based on the novel perspective of the candidate's electoral situation.

cs.CY↗

arXiv2023

Political Honeymoon Effect on Social Media: Characterizing Social Media Reaction to the Changes of Prime Minister in Japan

New leaders in democratic countries typically enjoy high approval ratings immediately after taking office. This phenomenon is called the honeymoon effect and is regarded as a significant political phenomenon; however, its mechanism remains underexplored. Therefore, this study examines how social media users respond to changes in political leadership in order to better understand the honeymoon effect in politics. In particular, we constructed a 15-year Twitter dataset on eight change timings of Japanese prime ministers consisting of 6.6M tweets and analyzed them in terms of sentiments, topics, and users. We found that, while not always, social media tend to show a honeymoon effect at the change timings of prime minister. The study also revealed that sentiment about prime ministers differed by topic, indicating that public expectations vary from one prime minister to another. Furthermore, the user base was largely replaced before and after the change in the prime minister, and their sentiment was also significantly different. The implications of this study would be beneficial for administrative management.

cs.SI↗

arXiv2022

Word Embedding for Social Sciences: An Interdisciplinary Survey

To extract essential information from complex data, computer scientists have been developing machine learning models that learn low-dimensional representation mode. From such advances in machine learning research, not only computer scientists but also social scientists have benefited and advanced their research because human behavior or social phenomena lies in complex data. However, this emerging trend is not well documented because different social science fields rarely cover each other's work, resulting in fragmented knowledge in the literature. To document this emerging trend, we survey recent studies that apply word embedding techniques to human behavior mining. We built a taxonomy to illustrate the methods and procedures used in the surveyed papers, aiding social science researchers in contextualizing their research within the literature on word embedding applications. This survey also conducts a simple experiment to warn that common similarity measurements used in the literature could yield different results even if they return consistent results at an aggregate level.

cs.AI↗

arXiv2022

Characterizing Human Actions in the Digital Platform by Temporal Context

Recent advances in digital platforms generate rich, high-dimensional logs of human behavior, and machine learning models have helped social scientists explain knowledge accumulation, communication, and information diffusion. Such models, however, almost always treat behavior as sequences of actions, abstracting the inter-temporal information among actions. To close this gap, we introduce a two-scale Action-Timing Context(ATC) framework that jointly embeds each action and its time interval. ATC obtains low-dimensional representations of actions and characterizes them with inter-temporal information. We provide three applications of ATC to real-world datasets and demonstrate that the method offers a unified view of human behavior. The presented qualitative findings demonstrate that explicitly modeling inter-temporal context is essential for a comprehensive, interpretable understanding of human activity on digital platforms.

cs.HC↗

arXiv2022

A Real-World Implementation of Unbiased Lift-based Bidding System

In display ad auctions of Real-Time Bid-ding (RTB), a typical Demand-Side Platform (DSP)bids based on the predicted probability of click and conversion right after an ad impression. Recent studies find such a strategy is suboptimal and propose a better bidding strategy named lift-based bidding.Lift-based bidding simply bids the price according to the lift effect of the ad impression and achieves maximization of target metrics such as sales. Despiteits superiority, lift-based bidding has not yet been widely accepted in the advertising industry. For one reason, lift-based bidding is less profitable for DSP providers under the current billing rule. Second, thepractical usefulness of lift-based bidding is not widely understood in the online advertising industry due to the lack of a comprehensive investigation of its impact.We here propose a practically-implementable lift-based bidding system that perfectly fits the current billing rules. We conduct extensive experiments usinga real-world advertising campaign and examine the performance under various settings. We find that lift-based bidding, especially unbiased lift-based bidding is most profitable for both DSP providers and advertisers. Our ablation study highlights that lift-based bidding has a good property for currently dominant first price auctions. The results will motivate the online

cs.IR↗

arXiv2021

Skyrmion-size dependence of the topological Hall effect: A real-space calculation

Motivated by the recent discoveries of magnets harboring short-pitch skyrmion lattices, we investigate the skyrmion-size dependence of the topological Hall effect. By means of large-scale real-space calculations, we find that the Hall conductivity takes its extreme value in the crossover region where both the real-space and momentum-space Berry curvature play a crucial role. We also investigate how the optimum skyrmion size ($λ_{\rm sk}^*$) depends on the lifetime of itinerant electrons ($τ$) and coupling constant between electrons and localized spins ($J$). For the former, we show that $λ_{\rm sk}^{*}$ is proportional to $\sqrtτ$, which indicates that $λ_{\rm sk}^{*}$ is much less sensitive to $τ$ than the conventional expectation that $λ_{\rm sk}^{*}$ is proportional to the mean-free path $\propto τ$. For the latter, we show that the non-adiabaticity considerably suppresses the topological Hall effect when the time scale determined by the skyrmion size and Fermi velocity is shorter than $1/J$. However, its effect on $λ_{\rm sk}^{*}$ is not so siginificant and $λ_{\rm sk}^{*}$ is about ten times the lattice constant in a wide range of $J$ and $τ$.

cond-mat.mes-hall↗

arXiv2021

Emergence of spin-orbit coupled ferromagnetic surface state derived from Zak phase in a nonmagnetic insulator FeSi

A chiral compound FeSi is a nonmagnetic narrow-gap insulator, exhibiting peculiar charge and spin dynamics beyond a simple band-structure picture. Those unusual features have been attracting renewed attention from topological aspects. Although a signature of surface conduction was indicated according to size-dependent resistivity in bulk crystals, its existence and topological properties remain elusive. Here we demonstrate an inherent surface ferromagnetic-metal state of FeSi thin films and its strong spin-orbit-coupling (SOC) properties through multiple characterizations of the two-dimensional (2D) conductance, magnetization and spintronic functionality. Terminated covalent-bonding orbitals constitute the polar surface state with momentum-dependent spin textures due to Rashba-type spin splitting, as corroborated by unidirectional magnetoresistance measurements and first-principles calculations. As a consequence of the spin-momentum locking, non-equilibrium spin accumulation causes magnetization switching. These surface properties are closely related to the Zak phase of the bulk band topology. Our findings propose another route to explore noble-metal-free materials for SOC-based spin manipulation.

cond-mat.str-el↗

arXiv2020

Online-to-Offline Advertisements as Field Experiments

Online advertisements have become one of today's most widely used tools for enhancing businesses partly because of their compatibility with A/B testing. A/B testing allows sellers to find effective advertisement strategies such as ad creatives or segmentations. Even though several studies propose a technique to maximize the effect of an advertisement, there is insufficient comprehension of the customers' offline shopping behavior invited by the online advertisements. Herein, we study the difference in offline behavior between customers who received online advertisements and regular customers (i.e., the customers visits the target shop voluntary), and the duration of this difference. We analyzed approximately three thousand users' offline behavior with their 23.5 million location records through 31 A/B testings. We first demonstrate the externality that customers with advertisements traverse larger areas than those without advertisements, and this spatial difference lasts several days after their shopping day. We then find a long-run effect of this externality of advertising that a certain portion of the customers invited to the offline shops revisit these shops. Finally, based on this revisit effect findings, we utilize a causal machine learning model to propose a marketing strategy to maximize the revisit ratio. Our results suggest that advertisements draw customers who have different behavior traits from regular customers. This study's findings demonstrate that a simple analysis may underrate the effects of advertisements on businesses, and an analysis considering externality can attract potentially valuable customers.

cs.LG↗