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Ousiometrics and Telegnomics: The essence of meaning conforms to a two-dimensional {powerful ⇔ weak} and {dangerous ⇔ safe} framework with diverse corpora presenting a safety bias

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arXiv 2021年

作者： Dodds, Peter Sheridan Alshaabi, Thayer Fudolig, Mikaela Irene Zimmerman, Julia Witte Lovato, Juniper Beaulieu, Shawn Minot, Joshua R. Arnold, Michael V. Reagan, Andrew J. Danforth, Christopher M. Computational Story Lab Vermont Advanced Computing Core University of Vermont BurlingtonVT05405 United States Vermont Complex Systems Center MassMutual Center of Excellence for Complex Systems and Data Science University of Vermont BurlingtonVT05405 United States Department of Computer Science University of Vermont BurlingtonVT05405 United States Computational Ethics Lab University of Vermont BurlingtonVT05405 United States Advanced Bioimaging Center UC Berkeley BerkeleyCA94720 United States MassMutual Data Science AmherstMA01002 United States Department of Mathematics & Statistics University of Vermont BurlingtonVT05405 United States

We define ‘ousiometrics’ to be the study of essential meaning in whatever context that meaningful signals are communicated, and ‘telegnomics’ as the study of remotely sensed knowledge. From work emerging through the middle of the 20th century, the essence of meaning has become generally accepted as being well captured by the three orthogonal dimensions of evaluation, potency, and activation (EPA). By re-examining first types and then tokens for the English language, and through the use of automatically annotated histograms—‘ousiograms’—we find here that: 1. The essence of meaning conveyed by words is instead best described by a compass-like power-danger (PD) framework with orthogonal axes described by the semantic differentials of {powerful ⇔ weak} and {dangerous ⇔ safe};and 2. Analysis of a disparate collection of large-scale English language corpora—literature, news, Wikipedia, talk radio, and social media—shows that natural language exhibits a systematic bias toward safe, low-danger words—a reinterpretation of the Pollyanna principle’s positivity bias for written expression. We identify and explore a third dimension representing structure, S. To help justify our choice of dimension names and to help address the problems with representing observed ousiometric dimensions by bipolar adjective pairs, we introduce and explore ‘synousionyms’ and ‘antousionyms’—ousiometric counterparts of synonyms and antonyms. We further show that the PD framework revises the circumplex model of affect as a more general model of state of mind. Finally, we use our findings to construct and test a prototype ‘ousiometer’, a telegnomic instrument that measures ousiometric time series for temporal corpora. We contend that our power-danger ousiometric framework provides a complement for entropy-based measurements, and may be of value for the study of a wide variety of communication across biological and artificial life. Copyright © 2021, The Authors. All rights reserved.

关键词： Semantics

A THERMODYNAMICALLY CONSISTENT PHASE-FIELD MODEL FOR MASS TRANSPORT WITH INTERFACIAL REACTION AND DEFORMATION

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arXiv 2025年

作者： Wang, Zhaoyang Huang, Huaxiong Lin, Ping Xu, Shixin School of Mathematical Sciences Laboratory of Mathematics and Complex Systems MOE Beijing Normal University Beijing100875 China Research Center for Mathematics Advanced Institute of Natural Sciences Beijing Normal University Guangdong Zhuhai519087 China Duke Kunshan University 8 Duke Ave. Jiangsu Kunshan215316 China Guandong Key Lab for Data Science and Technology BNU-HKBU United International College Guangdong Zhuhai China Department of Mathematics and Statistics York University TorontoONM3J 1P3 Canada Division of Mathematics University of Dundee DundeeDD1 4HN United Kingdom

In this paper, a thermodynamically consistent phase-field model is proposed to describe the mass transport and reaction processes of multiple species in a fluid. A key feature of this model is that reactions between different species occur only at the interface, and may induce deformation of the interface. For the governing equations derived based on the energy variational method, we propose a structure-preserving numerical scheme that satisfies the mass conservation and energy dissipation laws at the discrete level. Furthermore, we carry out a rigorous error analysis of the time-discrete scheme for a simplified case. A series of numerical experiments are conducted to validate the effectiveness of the model as well as the accuracy and stability of the scheme. In particular, we simulate microvessels with straight and bifurcated structures to illustrate the risk of microaneurysm formation. Copyright © 2025, The Authors. All rights reserved.

关键词： Specific energy

Computational timeline reconstruction of the stories surrounding Trump: Story turbulence, narrative control, and collective chronopathy

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arXiv 2020年

作者： Dodds, Peter Sheridan Minot, Joshua R. Arnold, Michael V. Alshaabi, Thayer Adams, Jane Lydia Reagan, Andrew J. Danforth, Christopher M. Computational Story Lab Vermont Complex Systems Center MassMutual Center of Excellence for Complex Systems and Data Science Vermont Advanced Computing Core University of Vermont BurlingtonVT05401 United States Department of Mathematics & Statistics University of Vermont BurlingtonVT05401 United States MassMutual Data Science BostonMA02110 United States

Measuring the specific kind, temporal ordering, diversity, and turnover rate of stories surrounding any given subject is essential to developing a complete reckoning of that subject’s historical impact. Here, we use Twitter as a distributed news and opinion aggregation source to identify and track the dynamics of the dominant day-scale stories around Donald Trump, the 45th President of the United States. Working with a data set comprising around 20 billion 1-grams, we first compare each day’s 1-gram and 2-gram usage frequencies to those of a year before, to create day- and week-scale timelines for Trump stories for 2016–2021. We measure Trump’s narrative control, the extent to which stories have been about Trump or put forward by Trump. We then quantify story turbulence and collective chronopathy—the rate at which a population’s stories for a subject seem to change over time. We show that 2017 was the most turbulent overall year for Trump. In 2020, story generation slowed dramatically during the first two major waves of the COVID-19 pandemic, with rapid turnover returning first with the Black Lives Matter protests following George Floyd’s murder and then later by events leading up to and following the 2020 US presidential election, including the storming of the US Capitol six days into 2021. Trump story turnover for 2 months during the COVID-19 pandemic was on par with that of 3 days in September 2017. Our methods may be applied to any well-discussed phenomenon, and have potential to enable the computational aspects of journalism, history, and biography. Copyright © 2020, The Authors. All rights reserved.

关键词： COVID-19

How the world’s collective attention is being paid to a pandemic: COVID-19 related 1-gram time series for 24 languages on Twitter

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arXiv 2020年

作者： Alshaabi, Thayer Arnold, Michael V. Minot, Joshua R. Adams, Jane Lydia Dewhurst, David Rushing Reagan, Andrew J. Muhamad, Roby Danforth, Christopher M. Dodds, Peter Sheridan Computational Story Lab Vermont Complex Systems Center MassMutual Center of Excellence for Complex Systems and Data Science Vermont Advanced Computing Core University of Vermont BurlingtonVT05401 MassMutual Data Science AmherstMA01002 International Class Program Faculty of Social and Political Sciences University of Indonesia Jakarta Indonesia Department of Mathematics & Statistics University of Vermont BurlingtonVT05401 United States

In confronting the global spread of the coronavirus disease COVID-19 pandemic we must have coordinated medical, operational, and political responses. In all efforts, data is crucial. Fundamentally, and in the possible absence of a vaccine for 12 to 18 months, we need universal, well-documented testing for both the presence of the disease as well as confirmed recovery through serological tests for antibodies, and we need to track major socioeconomic indices. But we also need auxiliary data of all kinds, including data related to how populations are talking about the unfolding pandemic through news and stories. To in part help on the social media side, we curate a set of 1000 day-scale time series of 1-grams across 24 languages on Twitter that are most ‘important’ for March 2020 with respect to March 2019. We determine importance through our allotaxonometric instrument, rank-turbulence divergence. We make some basic observations about some of the time series, including a comparison to numbers of confirmed deaths due to COVID-19 over time. We broadly observe across all languages a peak for the language-specific word for ‘virus’ in January followed by a decline through February and a recent surge through March. The world’s collective attention dropped away while the virus spread out from China. We host the time series on Gitlab, updating them on a daily basis while relevant. Our main intent is for other researchers to use these time series to enhance whatever analyses that may be of use during the pandemic as well as for retrospective investigations. Copyright © 2020, The Authors. All rights reserved.

关键词： Time series

Allotaxonometry and rank-turbulence divergence: A universal instrument for comparing complex systems

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arXiv 2020年

作者： Dodds, Peter Sheridan Minot, Joshua R. Arnold, Michael V. Alshaabi, Thayer Adams, Jane Lydia Dewhurst, David Rushing Gray, Tyler J. Frank, Morgan R. Reagan, Andrew J. Danforth, Christopher M. Computational Story Lab Vermont Complex Systems Center MassMutual Center of Excellence for Complex Systems and Data Science Vermont Advanced Computing Center University of Vermont BurlingtonVT05405 United States Department of Computer Science University of Vermont BurlingtonVT05405 United States Santa Fe Institute 1399 Hyde Park Rd Santa FeNM87501 United States Department of Mathematics & Statistics University of Vermont BurlingtonVT05405 United States Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA02139 United States MassMutual Data Science AmherstMA01002 United States Computational Story Lab Vermont Complex Systems Center MassMutual Center of Excellence for Complex Systems and Data Science Vermont Advanced Computing Center University of Vermont BurlingtonVT05405 United States Department of Mathematics & Statistics University of Vermont BurlingtonVT05405 United States

complex systems often comprise many kinds of components which vary over many orders of magnitude in size: Populations of cities in countries, individual and corporate wealth in economies, species abundance in ecologies, word frequency in natural language, and node degree in complex networks. Here, we introduce ‘allotaxonometry’ along with ‘rank-turbulence divergence’ (RTD), a tunable instrument for comparing any two ranked lists of components. We analytically develop our rank-based divergence in a series of steps, and then establish a rank-based allotaxonograph which pairs a map-like histogram for rank-rank pairs with an ordered list of components according to divergence contribution. We explore the performance of rank-turbulence divergence, which we view as an instrument of ‘type calculus’, for a series of distinct settings including: Language use on Twitter and in books, species abundance, baby name popularity, market capitalization, performance in sports, mortality causes, and job titles. We provide a series of supplementary flipbooks which demonstrate the tunability and storytelling power of rank-based allotaxonometry. Copyright © 2020, The Authors. All rights reserved.

关键词： Turbulence

Say their names: Resurgence in the collective attention toward black victims of fatal police violence following the death of george floyd

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arXiv 2021年

作者： Wu, Henry H. Gallagher, Ryan J. Alshaabi, Thayer Adams, Jane L. Minot, Joshua R. Arnold, Michael V. Welles, Brooke Foucault Harp, Randall Dodds, Peter Sheridan Danforth, Christopher M. Computational Story Lab MassMutual Center of Excellence for Complex Systems and Data Science Vermont Advanced Computing Core University of Vermont BurlingtonVT05401 United States Vermont Complex Systems Center University of Vermont BurlingtonVT05401 United States Communication Media and Marginalization Lab Network Science Institute Northeastern University BostonMA02115 United States Communication Studies Department Northeastern University BostonMA02115 United States Department of Philosophy University of Vermont BurlingtonVT05401 United States Department of Computer Science University of Vermont BurlingtonVT05401 United States Department of Mathematics & Statistics University of Vermont BurlingtonVT05401 United States

The murder of George Floyd by police in May 2020 sparked international protests and renewed attention in the Black Lives Matter movement. Here, we characterize ways in which the online activity following George Floyd's death was unparalleled in its volume and intensity, including setting records for activity on Twitter, prompting the saddest day in the platform's history, and causing George Floyd's name to appear among the ten most frequently used phrases in a day, where he is the only individual to have ever received that level of attention who was not known to the public earlier that same week. Further, we find this attention extended beyond George Floyd and that more Black victims of fatal police violence received attention following his death than during other past moments in Black Lives Matter's history. We place that attention within the context of prior online racial justice activism by showing how the names of Black victims of police violence have been lifted and memorialized over the last 12 years on Twitter. Our results suggest that the 2020 wave of attention to the Black Lives Matter movement centered past instances of police violence in an unprecedented way, demonstrating the impact of the movement's rhetorical strategy to "say their names. © 2021, CC BY-SA.

关键词： Law enforcement

Divergent modes of online collective attention to the COVID-19 pandemic are associated with future caseload variance

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arXiv 2020年

作者： Dewhurst, David Rushing Alshaabi, Thayer Arnold, Michael V. Minot, Joshua R. Danforth, Christopher M. Dodds, Peter Sheridan Vermont Complex Systems Center Computational Story Lab University of Vermont BurlingtonVT05405 MassMutual Data Science BostonMA02110 Department of Mathematics and Statistics University of Vermont BurlingtonVT05405

Using a random 10% sample of tweets authored from 2019-09-01 through 2020-04-30, we analyze the dynamic behavior of words (1-grams) used on Twitter to describe the ongoing COVID-19 pandemic. Across 24 languages, we find two distinct dynamic regimes: One characterizing the rise and subsequent collapse in collective attention to the initial Coronavirus outbreak in late January, and a second that represents March COVID-19-related discourse. Aggregating countries by dominant language use, we find that volatility in the first dynamic regime is associated with future volatility in new cases of COVID-19 roughly three weeks (average 22.49 ± 3.26 days) later. Our results suggest that surveillance of change in usage of epidemiology-related words on social media may be useful in forecasting later change in disease case numbers, but we emphasize that our current findings are not causal or necessarily predictive. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus

Micro-Macro Modeling of Polymeric Fluids and Shear-Induced Microscopic Behaviors

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arXiv 2023年

作者： Bao, Xuelian Huang, Huaxiong Song, Zilong Xu, Shixin School of Mathematical Sciences Beijing Normal University Beijing100875 China Research Centre for Mathematics Beijing Normal University Zhuhai China Guandong Key Lab for Data Science and Technology BNU-HKBU United International College Zhuhai China Laboratory of Mathematics and Complex Systems MOE Beijing Normal University Beijing100875 China Department of Mathematics and Statistics York University TorontoONM3J 1P3 Canada Mathematics and Statistics Department Utah State University Old Main Hill LoganUT84322 United States Duke Kunshan University 8 Duke Ave. Jiangsu Kunshan215316 China

This article delves into the micro-macro modeling of polymeric fluids, considering various microscopic potential energies, including the classical Hookean potential, as well as newly proposed modified Morse and Elastic-plastic potentials. These proposed potentials encompass microscopic-scale bond-breaking processes. The development of a thermodynamically consistent micro-macro model is revisited, employing the energy variational method. To validate the model’s predictions, we conduct numerical simulations utilizing a deterministic particle-FEM method. Our numerical findings shed light on the distinct behaviors exhibited by polymer chains at the micro-scale in comparison to the macro-scale velocity and induced shear stresses of fluids under shear flow. Notably, we observe that polymer elongation, rotation, and bond breaking contribute to the zero polymer-induced stress in the micro-macro model when employing Morse and Elastic-plastic potentials. Furthermore, at high shear rates, polymer rotation is found to induce shear-thinning behavior in the model employing the classical Hookean potential. Copyright © 2023, The Authors. All rights reserved.

关键词： Shear flow

Long-term word frequency dynamics derived from Twitter are corrupted: A bespoke approach to detecting and removing pathologies in ensembles of time series

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arXiv 2020年

作者： Dodds, Peter Sheridan Minot, Joshua R. Arnold, Michael V. Alshaabi, Thayer Adams, Jane Lydia Dewhurst, David Rushing Reagan, Andrew J. Danforth, Christopher M. Computational Story Lab Vermont Complex Systems Center MassMutual Center of Excellence for Complex Systems and Data Science Vermont Advanced Computing Core University of Vermont BurlingtonVT05401 Canada Department of Mathematics & Statistics University of Vermont BurlingtonVT05401 United States Charles River Analytics 625 Mount Auburn Street CambridgeMA02138 United States MassMutual Data Science AmherstMA01002 United States

Maintaining the integrity of long-term data collection is an essential scientific practice. As a field evolves, so too will that field’s measurement instruments and data storage systems, as they are invented, improved upon, and made obsolete. For data streams generated by opaque sociotechnical systems which may have episodic and unknown internal rule changes, detecting and accounting for shifts in historical datasets requires vigilance and creative analysis. Here, we show that around 10% of day-scale word usage frequency time series for Twitter collected in real time for a set of roughly 10,000 frequently used words for over 10 years come from tweets with, in effect, corrupted language labels. We describe how we uncovered problematic signals while comparing word usage over varying time frames. We locate time points where Twitter switched on or off different kinds of language identification algorithms, and where data formats may have changed. We then show how we create a statistic for identifying and removing words with pathological time series. While our resulting process for removing ‘bad’ time series from ensembles of time series is particular, the approach leading to its construction may be generalizeable. Copyright © 2020, The Authors. All rights reserved.

关键词： Time series