This study explores global citation diversity,examining its various patterns across countries and academic disciplines.We analyzed citation distributions in top institutes worldwide,revealing that the higher citation end of the distribution follow Power law or Pareto law pattern and the Pareto law's scaling exponent changes with the number of institutes considered.An entropy based novel citation inequality measure has been introduced, enhancing the precision of our analysis. Our findings show that countries with small and large economies often group similarly based on citation diversity, with shifting the groupings as the number of institutes considered changes.Moreover,we analyzed citation diversity among award-winning scientists across six scientific disciplines,finding significant variations.We also explored the evolution of citation diversity over the past century across multiple fields.A gender-based study in various disciplines highlights citation inequalities among male and female scientists.Our innovative citation diversity measure stands out as a vital tool for evaluating citation inequality,providing insights beyond what traditional citation counts can offer.This thorough analysis deepens our understanding of global scientific contributions and promotes a more equitable view of academic accomplishments.
{"title":"Exploring Citation Diversity in Scholarly Literature: An Entropy-Based Approach","authors":"suchismita Banerjee, Abhik Ghosh, Banasri Basu","doi":"arxiv-2409.02592","DOIUrl":"https://doi.org/arxiv-2409.02592","url":null,"abstract":"This study explores global citation diversity,examining its various patterns\u0000across countries and academic disciplines.We analyzed citation distributions in\u0000top institutes worldwide,revealing that the higher citation end of the\u0000distribution follow Power law or Pareto law pattern and the Pareto law's\u0000scaling exponent changes with the number of institutes considered.An entropy\u0000based novel citation inequality measure has been introduced, enhancing the\u0000precision of our analysis. Our findings show that countries with small and\u0000large economies often group similarly based on citation diversity, with\u0000shifting the groupings as the number of institutes considered\u0000changes.Moreover,we analyzed citation diversity among award-winning scientists\u0000across six scientific disciplines,finding significant variations.We also\u0000explored the evolution of citation diversity over the past century across\u0000multiple fields.A gender-based study in various disciplines highlights citation\u0000inequalities among male and female scientists.Our innovative citation diversity\u0000measure stands out as a vital tool for evaluating citation inequality,providing\u0000insights beyond what traditional citation counts can offer.This thorough\u0000analysis deepens our understanding of global scientific contributions and\u0000promotes a more equitable view of academic accomplishments.","PeriodicalId":501043,"journal":{"name":"arXiv - PHYS - Physics and Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142227449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Giordano De Marzo, Claudio Castellano, David Garcia
The applications of Large Language Models (LLMs) are going towards collaborative tasks where several agents interact with each other like in an LLM society. In such a setting, large groups of LLMs could reach consensus about arbitrary norms for which there is no information supporting one option over another, regulating their own behavior in a self-organized way. In human societies, the ability to reach consensus without institutions has a limit in the cognitive capacities of humans. To understand if a similar phenomenon characterizes also LLMs, we apply methods from complexity science and principles from behavioral sciences in a new approach of AI anthropology. We find that LLMs are able to reach consensus in groups and that the opinion dynamics of LLMs can be understood with a function parametrized by a majority force coefficient that determines whether consensus is possible. This majority force is stronger for models with higher language understanding capabilities and decreases for larger groups, leading to a critical group size beyond which, for a given LLM, consensus is unfeasible. This critical group size grows exponentially with the language understanding capabilities of models and for the most advanced models, it can reach an order of magnitude beyond the typical size of informal human groups.
{"title":"Language Understanding as a Constraint on Consensus Size in LLM Societies","authors":"Giordano De Marzo, Claudio Castellano, David Garcia","doi":"arxiv-2409.02822","DOIUrl":"https://doi.org/arxiv-2409.02822","url":null,"abstract":"The applications of Large Language Models (LLMs) are going towards\u0000collaborative tasks where several agents interact with each other like in an\u0000LLM society. In such a setting, large groups of LLMs could reach consensus\u0000about arbitrary norms for which there is no information supporting one option\u0000over another, regulating their own behavior in a self-organized way. In human\u0000societies, the ability to reach consensus without institutions has a limit in\u0000the cognitive capacities of humans. To understand if a similar phenomenon\u0000characterizes also LLMs, we apply methods from complexity science and\u0000principles from behavioral sciences in a new approach of AI anthropology. We\u0000find that LLMs are able to reach consensus in groups and that the opinion\u0000dynamics of LLMs can be understood with a function parametrized by a majority\u0000force coefficient that determines whether consensus is possible. This majority\u0000force is stronger for models with higher language understanding capabilities\u0000and decreases for larger groups, leading to a critical group size beyond which,\u0000for a given LLM, consensus is unfeasible. This critical group size grows\u0000exponentially with the language understanding capabilities of models and for\u0000the most advanced models, it can reach an order of magnitude beyond the typical\u0000size of informal human groups.","PeriodicalId":501043,"journal":{"name":"arXiv - PHYS - Physics and Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bálint Hartmann, Géza Ódor, Kristóf Benedek, István Papp
The dynamics of electric power systems are widely studied through the phase synchronization of oscillators, typically with the use of the Kuramoto equation. While there are numerous well-known order parameters to characterize these dynamics, shortcoming of these metrics are also recognized. To capture all transitions from phase disordered states over phase locking to fully synchronized systems, new metrics were proposed and demonstrated on homogeneous models. In this paper we aim to address a gap in the literature, namely, to examine how gradual improvement of power grid models affect the goodness of certain metrics. To study how the details of models are perceived by the different metrics, 12 variations of a power grid model were created, introducing varying level of heterogeneity through the coupling strength, the nodal powers and the moment of inertia. The grid models were compared using a second-order Kuramoto equation and adaptive Runge-Kutta solver, measuring the values of the phase, the frequency and the universal order parameters. Finally, frequency results of the models were compared to grid measurements. We found that the universal order parameter was able to capture more details of the grid models, especially in cases of decreasing moment of inertia. The most heterogeneous models showed very low synchronization and thus suggest a limitation of the second-order Kuramoto equation. Finally, we show local frequency results related to the multi-peaks of static models, which implies that spatial heterogeneity can also induce such multi-peak behavior.
{"title":"Power-grid modelling via gradual improvement of parameters","authors":"Bálint Hartmann, Géza Ódor, Kristóf Benedek, István Papp","doi":"arxiv-2409.02758","DOIUrl":"https://doi.org/arxiv-2409.02758","url":null,"abstract":"The dynamics of electric power systems are widely studied through the phase\u0000synchronization of oscillators, typically with the use of the Kuramoto\u0000equation. While there are numerous well-known order parameters to characterize\u0000these dynamics, shortcoming of these metrics are also recognized. To capture\u0000all transitions from phase disordered states over phase locking to fully\u0000synchronized systems, new metrics were proposed and demonstrated on homogeneous\u0000models. In this paper we aim to address a gap in the literature, namely, to\u0000examine how gradual improvement of power grid models affect the goodness of\u0000certain metrics. To study how the details of models are perceived by the\u0000different metrics, 12 variations of a power grid model were created,\u0000introducing varying level of heterogeneity through the coupling strength, the\u0000nodal powers and the moment of inertia. The grid models were compared using a\u0000second-order Kuramoto equation and adaptive Runge-Kutta solver, measuring the\u0000values of the phase, the frequency and the universal order parameters. Finally,\u0000frequency results of the models were compared to grid measurements. We found\u0000that the universal order parameter was able to capture more details of the grid\u0000models, especially in cases of decreasing moment of inertia. The most\u0000heterogeneous models showed very low synchronization and thus suggest a\u0000limitation of the second-order Kuramoto equation. Finally, we show local\u0000frequency results related to the multi-peaks of static models, which implies\u0000that spatial heterogeneity can also induce such multi-peak behavior.","PeriodicalId":501043,"journal":{"name":"arXiv - PHYS - Physics and Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Francesco Marzolla, Matteo Bruno, Hygor Piaget Monteiro Melo, Vittorio Loreto
The 15-minute city concept, advocating for cities where essential services are accessible within 15 minutes on foot and by bike, has gained significant attention in recent years. However, despite being celebrated for promoting sustainability, there is an ongoing debate regarding its effectiveness in reducing car usage and, subsequently, emissions in cities. In particular, large-scale evaluations of the effectiveness of the 15-minute concept in reducing emissions are lacking. To address this gap, we investigate whether cities with better walking accessibility, like 15-minute cities, are associated with lower transportation emissions. Comparing 700 cities worldwide, we find that cities with better walking accessibility to services emit less CO$_2$ per capita for transport. Moreover, we observe that among cities with similar average accessibility, cities spreading over larger areas tend to emit more. Our findings highlight the effectiveness of decentralised urban planning, especially the proximity-based 15-minute city, in promoting sustainable mobility. However, they also emphasise the need to integrate local accessibility with urban compactness and efficient public transit, which are vital in large cities.
{"title":"Compact 15-minute cities are greener","authors":"Francesco Marzolla, Matteo Bruno, Hygor Piaget Monteiro Melo, Vittorio Loreto","doi":"arxiv-2409.01817","DOIUrl":"https://doi.org/arxiv-2409.01817","url":null,"abstract":"The 15-minute city concept, advocating for cities where essential services\u0000are accessible within 15 minutes on foot and by bike, has gained significant\u0000attention in recent years. However, despite being celebrated for promoting\u0000sustainability, there is an ongoing debate regarding its effectiveness in\u0000reducing car usage and, subsequently, emissions in cities. In particular,\u0000large-scale evaluations of the effectiveness of the 15-minute concept in\u0000reducing emissions are lacking. To address this gap, we investigate whether\u0000cities with better walking accessibility, like 15-minute cities, are associated\u0000with lower transportation emissions. Comparing 700 cities worldwide, we find\u0000that cities with better walking accessibility to services emit less CO$_2$ per\u0000capita for transport. Moreover, we observe that among cities with similar\u0000average accessibility, cities spreading over larger areas tend to emit more.\u0000Our findings highlight the effectiveness of decentralised urban planning,\u0000especially the proximity-based 15-minute city, in promoting sustainable\u0000mobility. However, they also emphasise the need to integrate local\u0000accessibility with urban compactness and efficient public transit, which are\u0000vital in large cities.","PeriodicalId":501043,"journal":{"name":"arXiv - PHYS - Physics and Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Most complex systems can be captured by graphs or networks. Networks connect nodes (e.g. neurons) through edges (synapses), thus summarizing the system's structure. A popular way of interrogating graphs is community detection, which uncovers sets of geometrically related nodes. {em Geometric communities} consist of nodes ``closer'' to each other than to others in the graph. Some network features do not depend on node proximity -- rather, on them playing similar roles (e.g. building bridges) even if located far apart. These features can thus escape proximity-based analyses. We lack a general framework to uncover such features. We introduce {em topological communities}, an alternative perspective to decomposing graphs. We find clusters that describe a network as much as classical communities, yet are missed by current techniques. In our framework, each graph guides our attention to its relevant features, whether geometric or topological. Our analysis complements existing ones, and could be a default method to study networks confronted without prior knowledge. Classical community detection has bolstered our understanding of biological, neural, or social systems; yet it is only half the story. Topological communities promise deep insights on a wealth of available data. We illustrate this for the global airport network, human connectomes, and others.
{"title":"Topological communities in complex networks","authors":"Luís F Seoane","doi":"arxiv-2409.02317","DOIUrl":"https://doi.org/arxiv-2409.02317","url":null,"abstract":"Most complex systems can be captured by graphs or networks. Networks connect\u0000nodes (e.g. neurons) through edges (synapses), thus summarizing the system's\u0000structure. A popular way of interrogating graphs is community detection, which\u0000uncovers sets of geometrically related nodes. {em Geometric communities}\u0000consist of nodes ``closer'' to each other than to others in the graph. Some\u0000network features do not depend on node proximity -- rather, on them playing\u0000similar roles (e.g. building bridges) even if located far apart. These\u0000features can thus escape proximity-based analyses. We lack a general framework\u0000to uncover such features. We introduce {em topological communities}, an\u0000alternative perspective to decomposing graphs. We find clusters that describe a\u0000network as much as classical communities, yet are missed by current techniques.\u0000In our framework, each graph guides our attention to its relevant features,\u0000whether geometric or topological. Our analysis complements existing ones, and\u0000could be a default method to study networks confronted without prior knowledge.\u0000Classical community detection has bolstered our understanding of biological,\u0000neural, or social systems; yet it is only half the story. Topological\u0000communities promise deep insights on a wealth of available data. We illustrate\u0000this for the global airport network, human connectomes, and others.","PeriodicalId":501043,"journal":{"name":"arXiv - PHYS - Physics and Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Olumide Adisa, Enio Alterman Blay, Yasaman Asgari, Gabriele Di Bona, Samantha Dies, Ana Maria Jaramillo, Paulo H. Resende, Ana Maria de Sousa Leitao
Complexity science, despite its broad scope and potential impact, has not kept pace with fields like artificial intelligence, biotechnology and social sciences in addressing ethical concerns. The field lacks a comprehensive ethical framework, leaving us, as a community, vulnerable to ethical challenges and dilemmas. Other areas have gone through similar experiences and created, with discussions and working groups, their guides, policies and recommendations. Therefore, here we highlight the critical absence of formal guidelines, dedicated ethical committees, and widespread discussions on ethics within the complexity science community. Drawing on insights from the disciplines mentioned earlier, we propose a roadmap to enhance ethical awareness and action. Our recommendations include (i) initiating supportive mechanisms to develop ethical guidelines specific to complex systems research, (ii) creating open-access resources, and (iii) fostering inclusive dialogues to ensure that complexity science can responsibly tackle societal challenges and achieve a more inclusive environment. By initiating this dialogue, we aim to encourage a necessary shift in how ethics is integrated into complexity research, positioning the field to address contemporary challenges more effectively.
{"title":"The overlooked need for Ethics in Complexity Science: Why it matters","authors":"Olumide Adisa, Enio Alterman Blay, Yasaman Asgari, Gabriele Di Bona, Samantha Dies, Ana Maria Jaramillo, Paulo H. Resende, Ana Maria de Sousa Leitao","doi":"arxiv-2409.02002","DOIUrl":"https://doi.org/arxiv-2409.02002","url":null,"abstract":"Complexity science, despite its broad scope and potential impact, has not\u0000kept pace with fields like artificial intelligence, biotechnology and social\u0000sciences in addressing ethical concerns. The field lacks a comprehensive\u0000ethical framework, leaving us, as a community, vulnerable to ethical challenges\u0000and dilemmas. Other areas have gone through similar experiences and created,\u0000with discussions and working groups, their guides, policies and\u0000recommendations. Therefore, here we highlight the critical absence of formal\u0000guidelines, dedicated ethical committees, and widespread discussions on ethics\u0000within the complexity science community. Drawing on insights from the\u0000disciplines mentioned earlier, we propose a roadmap to enhance ethical\u0000awareness and action. Our recommendations include (i) initiating supportive\u0000mechanisms to develop ethical guidelines specific to complex systems research,\u0000(ii) creating open-access resources, and (iii) fostering inclusive dialogues to\u0000ensure that complexity science can responsibly tackle societal challenges and\u0000achieve a more inclusive environment. By initiating this dialogue, we aim to\u0000encourage a necessary shift in how ethics is integrated into complexity\u0000research, positioning the field to address contemporary challenges more\u0000effectively.","PeriodicalId":501043,"journal":{"name":"arXiv - PHYS - Physics and Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mark C. Ballandies, Dino Carpentras, Evangelos Pournaras
Decentralized autonomous organizations (DAOs) have transformed organizational structures by shifting from traditional hierarchical control to decentralized approaches, leveraging blockchain and cryptoeconomics. Despite managing significant funds and building global networks, DAOs face challenges like declining participation, increasing centralization, and inabilities to adapt to changing environments, which stifle innovation. This paper explores DAOs as complex systems and applies complexity science to explain their inefficiencies. In particular, we discuss DAO challenges, their complex nature, and introduce the self-organization mechanisms of collective intelligence, digital democracy, and adaptation. By applying these mechansims to improve DAO design and construction, a practical design framework for DAOs is created. This contribution lays a foundation for future research at the intersection of complexity science and DAOs.
去中心化自治组织（DAOs）利用区块链和加密经济学，从传统的等级控制转向去中心化方法，从而改变了组织结构。尽管DAO管理着大量资金并建立了全球网络，但它们仍面临着参与度下降、中心化加剧、无法适应不断变化的环境等挑战，从而扼杀了创新。本文将 DAO 视为复杂系统进行探讨，并应用复杂性科学解释其低效之处。我们特别讨论了 DAO 面临的挑战及其复杂性，并介绍了集体智慧、数字民主和适应性等自组织机制。通过应用这些机制来改进 DAO 的设计和构建，我们为 DAO 创建了一个实用的设计框架。这一贡献为未来复杂性科学与 DAO 的交叉研究奠定了基础。
{"title":"DAOs of Collective Intelligence? Unraveling the Complexity of Blockchain Governance in Decentralized Autonomous Organizations","authors":"Mark C. Ballandies, Dino Carpentras, Evangelos Pournaras","doi":"arxiv-2409.01823","DOIUrl":"https://doi.org/arxiv-2409.01823","url":null,"abstract":"Decentralized autonomous organizations (DAOs) have transformed organizational\u0000structures by shifting from traditional hierarchical control to decentralized\u0000approaches, leveraging blockchain and cryptoeconomics. Despite managing\u0000significant funds and building global networks, DAOs face challenges like\u0000declining participation, increasing centralization, and inabilities to adapt to\u0000changing environments, which stifle innovation. This paper explores DAOs as\u0000complex systems and applies complexity science to explain their inefficiencies.\u0000In particular, we discuss DAO challenges, their complex nature, and introduce\u0000the self-organization mechanisms of collective intelligence, digital democracy,\u0000and adaptation. By applying these mechansims to improve DAO design and\u0000construction, a practical design framework for DAOs is created. This\u0000contribution lays a foundation for future research at the intersection of\u0000complexity science and DAOs.","PeriodicalId":501043,"journal":{"name":"arXiv - PHYS - Physics and Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this work, we uncover patterns of usage mobile phone applications and information spread in response to perturbations caused by unprecedented events. We focus on categorizing patterns of response in both space and time and tracking their relaxation over time. To this end, we use the NetMob2023 Data Challenge dataset, which provides mobile phone applications traffic volume data for several cities in France at a spatial resolution of 100$m^2$ and a time resolution of 15 minutes for a time period ranging from March to May 2019. We analyze the spread of information before, during, and after the catastrophic Notre-Dame fire on April 15th and a bombing that took place in the city centre of Lyon on May 24th using volume of data uploaded and downloaded to different mobile applications as a proxy of information transfer dynamics. We identify different clusters of information transfer dynamics in response to the Notre-Dame fire within the city of Paris as well as in other major French cities. We find a clear pattern of significantly above-baseline usage of the application Twitter (currently known as X) in Paris that radially spreads from the area surrounding the Notre-Dame cathedral to the rest of the city. We detect a similar pattern in the city of Lyon in response to the bombing. Further, we present a null model of radial information spread and develop methods of tracking radial patterns over time. Overall, we illustrate novel analytical methods we devise, showing how they enable a new perspective on mobile phone user response to unplanned catastrophic events, giving insight into how information spreads during a catastrophe in both time and space.
{"title":"Detection of anomalous spatio-temporal patterns of app traffic in response to catastrophic events","authors":"Sofia Medina, Shazia'Ayn Babul, Rohit Sahasrabuddhe, Timothy LaRock, Renaud Lambiotte, Nicola Pedreschi","doi":"arxiv-2409.01355","DOIUrl":"https://doi.org/arxiv-2409.01355","url":null,"abstract":"In this work, we uncover patterns of usage mobile phone applications and\u0000information spread in response to perturbations caused by unprecedented events.\u0000We focus on categorizing patterns of response in both space and time and\u0000tracking their relaxation over time. To this end, we use the NetMob2023 Data\u0000Challenge dataset, which provides mobile phone applications traffic volume data\u0000for several cities in France at a spatial resolution of 100$m^2$ and a time\u0000resolution of 15 minutes for a time period ranging from March to May 2019. We\u0000analyze the spread of information before, during, and after the catastrophic\u0000Notre-Dame fire on April 15th and a bombing that took place in the city centre\u0000of Lyon on May 24th using volume of data uploaded and downloaded to different\u0000mobile applications as a proxy of information transfer dynamics. We identify\u0000different clusters of information transfer dynamics in response to the\u0000Notre-Dame fire within the city of Paris as well as in other major French\u0000cities. We find a clear pattern of significantly above-baseline usage of the\u0000application Twitter (currently known as X) in Paris that radially spreads from\u0000the area surrounding the Notre-Dame cathedral to the rest of the city. We\u0000detect a similar pattern in the city of Lyon in response to the bombing.\u0000Further, we present a null model of radial information spread and develop\u0000methods of tracking radial patterns over time. Overall, we illustrate novel\u0000analytical methods we devise, showing how they enable a new perspective on\u0000mobile phone user response to unplanned catastrophic events, giving insight\u0000into how information spreads during a catastrophe in both time and space.","PeriodicalId":501043,"journal":{"name":"arXiv - PHYS - Physics and Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142227424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Matteo Serafino, G. Virginio Clemente, James Flamino, Boleslaw K. Szymanski, Omar Lizardo, Hernan A. Makse
Since the advent of the internet, communication paradigms have continuously evolved, resulting in a present-day landscape where the dynamics of information dissemination have undergone a complete transformation compared to the past. In this study, we challenge the conventional two-step flow model of communication, a long-standing paradigm in the field. Our approach introduces a more intricate multi-step and multi-actor model that effectively captures the complexities of modern information spread. We test our hypothesis by examining the spread of information on the Twitter platform. Our findings support the multi-step and multi-actor model hypothesis. In this framework, influencers (individuals with a significant presence in social media) emerges as new central figures and partially take on the role previously attributed to opinion leaders. However, this does not apply to opinion leaders who adapt and reaffirm their influential position on social media, here defined as opinion-leading influencers. Additionally, we note a substantial number of adopters directly accessing information sources, suggesting a potentialdecline if influence in both opinion leaders and influencers. Finally, we found distinctions in the diffusion patterns of left- and right-leaning groups, indicating variations in the underlying structure of information dissemination across different ideologies.
{"title":"Analysis of flows in social media uncovers a new multi-step model of information spread","authors":"Matteo Serafino, G. Virginio Clemente, James Flamino, Boleslaw K. Szymanski, Omar Lizardo, Hernan A. Makse","doi":"arxiv-2409.01225","DOIUrl":"https://doi.org/arxiv-2409.01225","url":null,"abstract":"Since the advent of the internet, communication paradigms have continuously\u0000evolved, resulting in a present-day landscape where the dynamics of information\u0000dissemination have undergone a complete transformation compared to the past. In\u0000this study, we challenge the conventional two-step flow model of communication,\u0000a long-standing paradigm in the field. Our approach introduces a more intricate\u0000multi-step and multi-actor model that effectively captures the complexities of\u0000modern information spread. We test our hypothesis by examining the spread of\u0000information on the Twitter platform. Our findings support the multi-step and\u0000multi-actor model hypothesis. In this framework, influencers (individuals with\u0000a significant presence in social media) emerges as new central figures and\u0000partially take on the role previously attributed to opinion leaders. However,\u0000this does not apply to opinion leaders who adapt and reaffirm their influential\u0000position on social media, here defined as opinion-leading influencers.\u0000Additionally, we note a substantial number of adopters directly accessing\u0000information sources, suggesting a potentialdecline if influence in both opinion\u0000leaders and influencers. Finally, we found distinctions in the diffusion\u0000patterns of left- and right-leaning groups, indicating variations in the\u0000underlying structure of information dissemination across different ideologies.","PeriodicalId":501043,"journal":{"name":"arXiv - PHYS - Physics and Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Csegő Balázs Kolok, Gergely Ódor, Dániel Keliger, Márton Karsai
We study stationary epidemic processes in scale-free networks with local awareness behavior adopted by only susceptible, only infected, or all nodes. We find that while the epidemic size in the susceptible-aware and the all-aware scenarios scales linearly with the network size, the scaling becomes sublinear in the infected-aware scenario, suggesting that fewer aware nodes may reduce the epidemic size more effectively. We explain this paradox via numerical and theoretical analysis, and highlight the role of influential nodes and their disassortativity to raise awareness in epidemic scenarios.
{"title":"Epidemic paradox induced by awareness driven network dynamics","authors":"Csegő Balázs Kolok, Gergely Ódor, Dániel Keliger, Márton Karsai","doi":"arxiv-2409.01384","DOIUrl":"https://doi.org/arxiv-2409.01384","url":null,"abstract":"We study stationary epidemic processes in scale-free networks with local\u0000awareness behavior adopted by only susceptible, only infected, or all nodes. We\u0000find that while the epidemic size in the susceptible-aware and the all-aware\u0000scenarios scales linearly with the network size, the scaling becomes sublinear\u0000in the infected-aware scenario, suggesting that fewer aware nodes may reduce\u0000the epidemic size more effectively. We explain this paradox via numerical and\u0000theoretical analysis, and highlight the role of influential nodes and their\u0000disassortativity to raise awareness in epidemic scenarios.","PeriodicalId":501043,"journal":{"name":"arXiv - PHYS - Physics and Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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