arXiv subjects

Yasuko Kawahata

Publications and source records attributed to Yasuko Kawahata.

At least 37 records · Page 2Linked to original sources

arXiv2024

Note: Harnessing Tellurium Nanoparticles in the Digital Realm Plasmon Resonance, in the Context of Brewster's Angle and the Drude Model for Fake News Adsorption in Incomplete Information Games

This note explores the innovative application of soliton theory and plasmonic phenomena in modeling user behavior and engagement within digital health platforms. By introducing the concept of soliton solutions, we present a novel approach to understanding stable patterns of health improvement behaviors over time. Additionally, we delve into the role of tellurium nanoparticles and their plasmonic properties in adsorbing fake news, thereby influencing user interactions and engagement levels. Through a theoretical framework that combines nonlinear dynamics with the unique characteristics of tellurium nanoparticles, we aim to provide new insights into the dynamics of user engagement in digital health environments. Our analysis highlights the potential of soliton theory in capturing the complex, nonlinear dynamics of user behavior, while the application of plasmonic phenomena offers a promising avenue for enhancing the sensitivity and effectiveness of digital health platforms. This research ventures into an uncharted territory where optical phenomena such as Brewster's Angle and Snell's Law, along with the concept of spin solitons, are metaphorically applied to address the challenge of fake news dissemination. By exploring the analogy between light refraction, reflection, and the propagation of information in digital platforms, we unveil a novel perspective on how the 'angle' at which information is presented can significantly affect its acceptance and spread. Additionally, we propose the use of tellurium nanoparticles to manage 'information waves' through mechanisms akin to plasmonic resonance and soliton dynamics. This theoretical exploration aims to bridge the gap between physical sciences and digital communication, offering insights into the development of strategies for mitigating misinformation.

physics.soc-ph↗

arXiv2024

Plasmon Resonance Model: Investigation of Analysis of Fake News Diffusion Model with Third Mover Intervention Using Soliton Solution in Non-Complete Information Game under Repeated Dilemma Condition

In this research note, we propose a new approach to model the fake news diffusion process within the framework of incomplete information games. In particular, we use nonlinear partial differential equations to represent the phenomenon of plasmon resonance, in which the diffusion of fake news is rapidly amplified within a particular social group or communication network, and analyze its dynamics through a soliton solution approach. In addition, we consider how first mover, second mover, and third mover strategies interact within this nonlinear system and contribute to the amplification or suppression of fake news diffusion. The model aims to understand the mechanisms of fake news proliferation and provide insights into how to prevent or combat it. By combining concepts from the social sciences and the physical sciences, this study attempts to develop a new theoretical framework for the contemporary problem of fake news.This paper is partially an attempt to utilize "Generative AI" and was written with educational intent. There are currently no plans for it to become a peer-reviewed paper.

physics.soc-ph↗

arXiv2024

Introducing First-Principles Calculations: New Approach to Group Dynamics and Bridging Social Phenomena in TeNP-Chain Based Social Dynamics Simulations

This note considers an innovative interdisciplinary methodology that bridges the gap between the fundamental principles of quantum mechanics applied to the study of materials such as tellurium nanoparticles (TeNPs) and graphene and the complex dynamics of social systems. The basis for this approach lies in the metaphorical parallels drawn between the structural features of TeNPs and graphene and the behavioral patterns of social groups in the face of misinformation. TeNPs exhibit unique properties such as the strengthening of covalent bonds within telluric chains and the disruption of secondary structure leading to the separation of these chains. This is analogous to increased cohesion within social groups and disruption of information flow between different subgroups, respectively. . Similarly, the outstanding properties of graphene, such as high electrical conductivity, strength, and flexibility, provide additional aspects for understanding the resilience and adaptability of social structures in response to external stimuli such as fake news. This research note proposes a novel metaphorical framework for analyzing the spread of fake news within social groups, analogous to the structural features of telluric nanoparticles (TeNPs). We investigate how the strengthening of covalent bonds within TeNPs reflects the strengthening of social cohesion in groups that share common beliefs and values. This paper is partially an attempt to utilize "Generative AI" and was written with educational intent. There are currently no plans for it to become a peer-reviewed paper.

physics.soc-ph↗

arXiv2024

Pathfinding of Digital False Information Diffusion by Conformal Field Theory and Feynman's Green's Function: Multiple Analysis Considering Stress Energy Tensor and Symmetry of Virasoro Operators

This study delves into the application of Conformal Field Theory (CFT) to understand information diffusion within digital media and its broader social implications. Focusing on the digital native generation, vulnerable to misinformation and data privacy issues, the research highlights the urgency of addressing psychological health in the digital realm. By leveraging CFT's analytical power, particularly in tracing information flows and assessing the impact of misinformation, the study offers novel insights into the dynamics of digital communication. It proposes a model for predicting and analyzing the spread of biased content, emphasizing the need for improved information literacy and personal data protection. This paper explores the concept of infinitesimal conformal transformations using the Virasoro operator, forming a conformal transformation group. In the examination of multiple analytical methods, the approach to quantitatively analyze cases that are likely to expose the risk of spreading filter bubbles such as negative information, rumors, and fake news is to incorporate a two-dimensional conformal field theory approach using the Ward-Takahashi identity and path integral with trace anomalies and metric are introduced. Trace anomalies and metrics were introduced. A trace anomaly refers to the spread of information in an unexpected way, usually indicating anomalous behavior or hidden dynamics in the system. Metric variation hypothesized and analyzed how diffusion characteristics, such as the rate and pattern of information diffusion, vary over time.

physics.soc-ph↗

arXiv2024

Discussion of Loop Expansion and Introduction of Series Cutting Functions to Local Potential Approximation: Complexity Analysis Using Green's Functions, Cutting Of Nth-Order Social Interactions For Progressive Safety

In this study, we focus on the aforementioned paper, "Examination Kubo-Matsubara Green's Function Of The Edwards-Anderson Model: Extreme Value Information Flow Of Nth-Order Interpolated Extrapolation Of Zero Phenomena Using The Replica Method (2024)". This paper also applies theoretical physics methods to better understand the filter bubble phenomenon, focusing in particular on loop expansions and truncation functions. Using the loop expansion method, the complexity of social interactions during the occurrence of filter bubbles will be discussed in order to introduce series, express mathematically, and evaluate the impact of these interactions. We analyze the interactions between agents and their time evolution using a variety of Green's functions, including delayed Green's functions, advanced Green's functions, and causal Green's functions, to capture the dynamic response of the system through local potential approximations. In addition, we apply truncation functions and truncation techniques to ensure incremental safety and evaluate the long-term stability of the system. This approach will enable a better understanding of the mechanisms of filter bubble generation and dissolution, and discuss insights into their prevention and management. This research explores the possibilities of applying theoretical physics frameworks to social science problems and examines methods for analyzing the complex dynamics of information flow and opinion formation in digital society.This paper is partially an attempt to utilize "Generative AI" and was written with educational intent. There are currently no plans for it to become a peer-reviewed paper.

physics.soc-ph↗

arXiv2024

Repetitive Dilemma Games in Distribution Information Using Interplay of Droop Quota: Meek's Method in Impact of Maximum Compensation and Minimum Cost Routes in Information Role of Marginal Contribution in Two-Sided Matching Markets

This paper is a preliminary report of the research plan and a digest of the results and discussions. On research note explores the complex dynamics of fake news dissemination and fact-checking costs within the framework of information markets and analyzes the equilibrium between supply and demand using the concepts of droop quotas, Meek's method, and marginal contributions. By adopting a two-sided matching market perspective, we delve into scenarios in which markets are stable under the influence of fake news perceived as truth and those in which credibility prevails. Through the application of iterated dilemma game theory, we investigate the strategic choices of news providers affected by the costs associated with spreading fake news and fact-checking efforts. We further examine the maximum reward problem and strategies to minimize the cost path for spreading fake news, and consider a nuanced understanding of market segmentation into "cheap" and "premium" segments based on the nature of the information being spread. Our analysis uses mathematical models and computational processes to identify stable equilibrium points that ensure market stability in the face of deceptive information practices and provide insight into effective strategies to enhance the informational health of the market. Through this comprehensive approach, this paper aims for a more truthful and reliable perspective from which to observe information markets. This paper is partially an attempt to utilize "Generative AI" and was written with educational intent. There are currently no plans for it to become a peer-reviewed paper.

econ.GN↗

arXiv2024

Note: Bridging Information Science: AB Initio Calculation Vortex of 2D Materials of Bismuthene(Bismuth Molecule) Graphene-Shaped through Kohn-Sham Equations

This study delves into the intricate electronic and optical behaviors of two-dimensional (2D) honeycomb materials, such as Stannen, arsenene, antimonene, silicene, and bismuthene(bismuth molecule), through the lens of first-principles calculations(AB Initio Calculations) based on the Kohn-Sham equations. Focusing on the exchange-correlation potential approximations within the Density Functional Theory (DFT) framework, we evaluate the potential of these materials in digital information control and management. Special attention is given to the nonlinear optical responses and electronic properties under the influence of twisted bilayer configurations, external fields, and varying twist angles. The findings offer novel insights into the design of advanced digital devices, suggesting a transformative approach to information technology through the utilization of 2D honeycomb materials.

cond-mat.mtrl-sci↗

arXiv2024

Note: Applying the Brillouin Zone and Band Gap Leveraging AB Initio Calculation for Digital Well-Being: In-Depth Analysis of Band Structures in Information Spaces Insights from Solid-State Physics

The efforts of this Note are aimed at understanding various phenomena in digital space that are incomplete and difficult to define, and to translate them into language from research fields that are based on existing large-scale experimental data. Information diffusion and user behavior patterns in digital space are often hard to intuitively capture, and the principles and mechanisms behind them are difficult to articulate. To address this challenge, we have drawn on first-principles methods in physics, particularly solid state physics. This approach is known to be effective in analyzing the behavior of real physical materials at the atomic level and in understanding the electronic properties and bonding structures of materials. We have attempted to apply physical concepts based on first-principles calculations, in particular concepts such as first Brillouin zones, band gaps, and reciprocal lattice vectors, to digital space as a metaphor. This theoretical framework allows us to quantitatively and logically infer the behavior and trends of incomplete and ambiguous digital data and phenomena. For example, the "first Brillouin zone" of information in digital media indicates the potential sphere of influence of that information, and the band gap defines the threshold of influence for the information to be widely accepted. In addition, the reciprocal lattice vector serves as a boundary that indicates the limits of information characteristics and is an indicator of the degree to which a particular piece of information resonates with users. With this theoretical supplement, our research goes beyond the mere analysis of phenomena in the digital space and provides new methods for understanding and predicting the behavior of imperfect data and uncertain digital media.

cond-mat.mtrl-sci↗

arXiv2023

Discussion Model for Propagation of Social Opinion via Quantum Galois Noise Channels:Entanglement, SuperSpreader

We apply the concepts of classical and quantum channels to the modeling of opinion dynamics and propose a stochastic method for representing the temporal variation of individual and group opinions. In particular, we use quantum Galois noise channels to couple quantum information theory with social interaction to construct a new model of opinion dynamics that accounts for error rates and noise effects. This model captures more complex opinion propagation and interaction by incorporating the concepts of partial traces and entanglement. We also consider the role of superspreaders in the propagation of noisy information and their suppression mechanisms, and represent these dynamics in a mathematical model. We model the influence of superspreaders on interactions between individuals using unitary transformations and propose a new approach to measure social trustworthiness. In addition, we elaborate on the modeling of opinion propagation and suppression using Holevo channels. These models provide a new framework for a better understanding of social interactions and expand the potential applications of quantum information theory.

physics.soc-ph↗

arXiv2023

From Spin States to Social Consensus: Ising Approach to Dimer Configurations in Opinion Formation

The field of opinion dynamics has evolved steadily since the earliest studies applying magnetic physics methods to better understand social opinion formation. However, in the real world, complete agreement of opinions is rare, and biaxial consensus, especially on social issues, is rare. To address this challenge, Ishii and Kawabata (2018) proposed an extended version of the Bounded Confidence Model that introduces new parameters indicating dissent and distrust, as well as the influence of mass media. Their model aimed to capture more realistic social opinion dynamics by introducing coefficients representing the degree of trust and distrust, rather than assuming convergence of opinions. In this paper, we propose a new approach to opinion dynamics based on this Trust-Distrust Model (TDM), applying the dimer allocation and Ising model. Our goal is to explore how the interaction between trust and distrust affects social opinion formation. In particular, we analyze through mathematical models how various external stimuli, such as mass media, third-party opinions, and economic and political factors, affect people's opinions. Our approach is to mathematically represent the dynamics of trust and distrust, which traditional models have not addressed. This theoretical framework provides new insights into the polarization of opinions, the process of consensus building, and how these are reflected in social behavior. In addition to developing the theoretical framework by applying the dimer configuration, the dimer model and the Ising model, this paper uses numerical simulations to show how the proposed model applies to actual social opinion formation. This research aims to contribute to a deeper understanding of social opinion formation by providing new perspectives in the fields of social science, physics, and computational modeling.

physics.soc-ph↗

arXiv2023

Modeling Opinion Toroidal Polarization: Insights from Bounding Confidence Beyond, Distance Matters

Deterministic dynamics is a mathematical model used to describe the temporal evolution of a system, generally expressed as dx/dt = F(x), where x represents the system's state, and F(x) determines its dynamics. It is employed to understand long-term system behavior, including opinion formation and polarization in online communities. Opinion dynamics models, like the Katz model and the logistic map, help analyze how individual opinions are influenced within social networks and exhibit chaotic behavior. These models are crucial for studying opinion formation and collective behavior on social media, especially in conjunction with branching theory. For instance, Galam's Ising model applies principles from physics to social sciences, representing individual opinions as "spins" and illustrating how local interactions influence consensus formation. The Bounding Confidence model considers opinions within a confidence interval, showing how opinions converge or polarize. These models effectively analyze opinion dynamics in online communities, aiding in understanding trends and viral phenomena on social media. This research aims to analyze discourse flow and opinion evolution, predicting future trends in online communities and decoding digital-age human interaction dynamics. Combining branching theory with opinion dynamics models enhances our understanding of digital communication. In the modified opinion dynamics model, the weight parameter h for distance introduces distance-based interaction terms. The update equation is adjusted to control interaction strength based on distance, with hij calculated from the distance dij. This customization allows for a more accurate representation of opinion dynamics in specific scenarios, forming the basis for discussions in this paper.

physics.soc-ph↗

arXiv2023

From Stability to Change: The Potential Application of Bifurcation Theory to Opinion Dynamics Considerations

This study adopts a nonlinear dynamics approach, specifically using bifurcation theory, to analyze social interactions and behavior in online communities. Referencing key works by Steven Strogatz and others, the paper explores the application of pitchfork, saddle node, and transcritical bifurcations to model collective opinion shifts and trend diffusion in social media. By integrating Strogatz's insights on complex networks and synchronization with the foundational theories of Guckenheimer, Holmes, Kuznetsov, and Wiggins, the study examines the role of small-world network effects and synchronization in collective behavior. Seidel's practical take on bifurcation theory helps apply these mathematical concepts to social science research, aiming to shed light on the dynamics of digital communication and the rapid spread of information. The research also touches on the use of Melnikov's method for analyzing the stability of homoclinic and heteroclinic orbits, and the onset of chaos, reflecting on the broader implications for understanding complex systems in nature and society. The goal is to provide a model that captures the swift and intricate cognitive processes of individuals in digital ecosystems, offering a fresh perspective on the evolution of online collective opinion, the rise and spread of fads, and behavioral changes in digital contexts.

physics.soc-ph↗

arXiv2023

Transitioning To The Digital Generation Case Studies (Previous Digital Point Studies In Japan Cases:1993-2023)

In this paper, we discuss at The 8th International Workshop on Application of Big Data for Computational Social Science, October 26-29, 2023, Venice, Italy. To achieve the realization of the Global and Innovation Gateway for All (GIGA) initiative (2019), proposed in December 2019 by the Primary and Secondary Education Planning Division of the Elementary and Secondary Education Bureau of the Ministry of Education, Culture, Sports, Science and Technology, a movement has emerged to utilize information and communication technology (ICT) in the field of education. The history of ICT education in Japan dates back to the 100 Schools Project (1994), which aimed to provide network access environments, and the New 100 Schools Project (1997), which marked the beginning of full-scale ICT education in Japan. In this paper, we discuss the usage dynamics of smartphone-based learning applications among young people (analyzing data from January to September 2020) and their current status. Further, the results are summarized and future research topics and issues are discussed. The results show that there are situations in which ICT learning environments can be effectively utilized and others in which they cannot, depending on the differences between digital students and analog students who utilize ICT in their studies; this indicates that we are currently in a transition to a generation of digital natives. ICT education has both advantages and disadvantages, and it is expected that it will be used in combination with conventional educational methods while assessing the characteristics of ICT education in the future. Of course, there are many challenges. We plan to discuss how to appeal in this regard at the Workshop.

cs.CY↗

arXiv2021

The State of the Art when using GPUs in Devising Image Generation Methods Using Deep Learning

Deep learning is a technique for machine learning using multi-layer neural networks. It has been used for image synthesis and image recognition, but in recent years, it has also been used for various social detection and social labeling. In this analysis, we compared (1) the number of Iterations per minute between the GPU and CPU when using the VGG model and the NIN model, and (2) the number of Iterations per minute by the number of pixels when using the VGG model, using an image with 128 pixels. When the number of pixels was 64 or 128, the processing time was almost the same when using the GPU, but when the number of pixels was changed to 256, the number of iterations per minute decreased and the processing time increased by about three times. In this case study, since the number of pixels becomes core dumping when the number of pixels is 512 or more, we can consider that we should consider improvement in the vector calculation part. If we aim to achieve 8K highly saturated computer graphics using neural networks, we will need to consider an environment that allows computation even when the size of the image becomes even more highly saturated and massive, and parallel computation when performing image recognition and tuning.

cs.CV↗

arXiv2019

A study of trends in the effects of TV ratings and social media (Twitter) -- Case study 1

The Japanese TV program 'Drama A' is a drama broadcast from October to December 2016. The audience rating was sluggish, but this drama marked a high audience rating in 2016. Since it was popular from the middle, and it was speculated that there was a part related to social media in the popularity, we considered existing research methods as a case study. In this paper, we used a mathematical model of the hit phenomenon to examine the impact of audience assessment from social media from a sociophysical perspective. We got the same consideration as the audience rating per minute of video research. This paper is IEEE BIGDATA2018's Revised paper(Consideration on TV audience rating and influence of social media).

cs.SI↗

arXiv2019

Consensus formation Online using Sociophysics method

Consensus formation and difference of opinion have long been the subject of research. However, relevant laws and systems within society are being updated to reflect the changes in information networks. Online environment has come to fulfill a major role as a real and concrete place of opposing opinions and consensus formation. In the future, quantitative findings on consensus formation, and findings on relevant trends, must be summarized, and quantitative research related to trends likely to give rise to social and economic risk is required. Thus, the potential for comparing research related to consensus formation using actual data and an approach using a mathematical model was first investigated.

cs.SI↗

arXiv2018

Opinion Dynamics Theory for Analysis of Consensus Formation and Division of Opinion on the Internet

The massive amount of text data on the web has facilitated research on the quantitative analysis of public opinion, which could not be visualized earlier. In this paper, we propose a new opinion dynamics theory. This theory that is intended to explain agreement formation and opinion breakup division in opinion exchanges on social media such as Twitter. With the popularization of the public network, we have become able to communicate with instantaneity and interactivity beyond the temporal and spatial constraints.Research on quantitatively analyzing the distribution of opinion on public opinion that has not been visualized so far utilizing massive web text data is progressing.Our model is based on the Bounded Confidence Model, that expresses opinions in as continuous quantity values. However, in the Bounded Confidence Model, it was assumed that people with different opinions move not in disregard but ignoring opinions. Furthermore, in our theory, it modeled so that it can expresser model incorporates the influence from of the external pressure outside and the phenomenon depending on the surrounding situation.

physics.soc-ph↗

arXiv2018

Analysis of social media content and search behavior related to seasonal topics using the sociophysics approach

We studied the time interval between posting social media content and search action related to seasonal topics. The analysis was performed using a mathematical model of the search behavior as in the theory of sociophysics. As seasonal topics, the word cherry blossom was considered for spring, bikini for summer, autumn leaves for fall, and skiing for winter. We examined the influence of blogs and Twitter posts given the search behavior and found a time deviation of interest on these topics.

physics.soc-ph↗