检索结果-内蒙古大学图书馆

The sleep loss insult of spring daylight savings in the US is absorbed by Twitter users within 48 hours

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Linnell, Kelsey Alshaabi, Thayer McAndrew, Thomas Lim, Jeanie Dodds, Peter Sheridan Danforth, Christopher M. Computational Story Lab Vermont Complex Systems Center MassMutual Center of Excellence for Complex Systems & Data Science University of Vermont Department of Mathematics & Statistics University of Vermont Department of Biostatistics & Epidemiology School of Public Health & Health Sciences University of Massachusetts at Amherst MassMutual Data Science

Sleep loss has been linked to heart disease, diabetes, cancer, and an increase in accidents, all of which are among the leading causes of death in the United States. Population-scale sleep studies have the potential to advance public health by helping to identify at-risk populations, changes in collective sleep patterns, and to inform policy change. Prior research suggests other kinds of health indicators such as depression and obesity can be estimated using social media activity. However, the inability to effectively measure collective sleep with publicly available data has limited large-scale academic studies. Here, we investigate the passive estimation of sleep loss through a proxy analysis of Twitter activity profiles. We use "Spring Forward" events, which occur at the beginning of Daylight Savings Time in the United States, as a natural experimental condition to estimate spatial differences in sleep loss across the United States. On average, peak Twitter activity occurs roughly 45 minutes later on the Sunday following Spring Forward. By Monday morning however, activity curves are realigned with the week before, suggesting that at least on Twitter, the lost hour of early Sunday morning has been quickly absorbed. Copyright © 2020, The Authors. All rights reserved.

关键词： Sleep research

Spontaneous flow instabilities of active polar fluids in three dimensions

学校读者我要写书评

暂无评论

Physical Review Research 2023年第2期5卷 L022061-L022061页

作者： Abhinav Singh Quentin Vagne Frank Jülicher Ivo F. Sbalzarini Faculty of Computer Science Technische Universität Dresden Dresden Germany Max Planck Institute of Molecular Cell Biology and Genetics Dresden Germany Center for Systems Biology Dresden Dresden Germany Center for Scalable Data Analytics and Artificial Intelligence ScaDS.AI Dresden/Leipzig Germany University of Geneva Geneva Switzerland Max Planck Institute for the Physics of Complex Systems Dresden Germany Cluster of Excellence Physics of Life TU Dresden Dresden Germany

Active polar fluids exhibit spontaneous flow when sufficient active stress is generated by internal molecular mechanisms. This is also referred to as an active Fréedericksz transition. Experiments have revealed the existence of competing in-plane and out-of-plane instabilities in three-dimensional active matter. So far, however, a theoretical model reconciling all observations is missing. In particular, the role of boundary conditions in these instabilities still needs to be explained. Here, we characterize the spontaneous flow transition in a symmetry-preserving three-dimensional active Ericksen-Leslie model, showing that the boundary conditions select the emergent behavior. Using nonlinear numerical solutions and linear perturbation analysis, we explain the mechanism for both in-plane and out-of-plane instabilities under extensile active stress for perpendicular polarity anchoring at the boundary, whereas parallel anchoring only permits in-plane flows under contractile stress or out-of-plane wrinkling under extensile stress.

关键词： Flow instability Active matter Active polar gels Biological materials Perturbative methods

Sentiment and structure in word co-occurrence networks on Twitter

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Fudolig, Mikaela Irene Alshaabi, Thayer Arnold, Michael V. 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 BurlingtonVT United States Advanced Bioimaging Center UC Berkeley BerkeleyCA United States Department of Mathematics & Statistics University of Vermont BurlingtonVT United States Department of Computer Science University of Vermont BurlingtonVT United States

We explore the relationship between context and happiness scores in political tweets using word co-occurrence networks, where nodes in the network are the words, and the weight of an edge is the number of tweets in the corpus for which the two connected words co-occur. In particular, we consider tweets with hashtags #imwithher and #crookedhillary, both relating to Hillary Clinton’s presidential bid in 2016. We then analyze the network properties in conjunction with the word scores by comparing with null models to separate the effects of the network structure and the score distribution. Neutral words are found to be dominant and most words, regardless of polarity, tend to co-occur with neutral words. We do not observe any score homophily among positive and negative words. However, when we perform network backboning, community detection results in word groupings with meaningful narratives, and the happiness scores of the words in each group correspond to its respective theme. Thus, although we observe no clear relationship between happiness scores and co-occurrence at the node or edge level, a community-centric approach can isolate themes of competing sentiments in a corpus. © 2021, CC BY.

关键词：

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

学校读者我要写书评

暂无评论

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

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

学校读者我要写书评

暂无评论

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

Computational Paremiology: Charting the temporal, ecological dynamics of proverb use in books, news articles, and tweets

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Davis, Ethan Danforth, Christopher M. Mieder, Wolfgang 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 United States Department of Mathematics and Statistics University of Vermont BurlingtonVT05401 United States Department of German and Russian University of Vermont BurlingtonVT05401 United States Department of Computer Science University of Vermont BurlingtonVT05401 United States

Proverbs are an essential component of language and culture, and though much attention has been paid to their history and currency, there has been comparatively little quantitative work on changes in the frequency with which they are used over time. With wider availability of large corpora reflecting many diverse genres of documents, it is now possible to take a broad and dynamic view of the importance of the proverb. Here, we measure temporal changes in the relevance of proverbs within three corpora, differing in kind, scale, and time frame: Millions of books over centuries;hundreds of millions of news articles over twenty years;and billions of tweets over a decade. We find that proverbs present heavy-tailed frequency-of-usage rank distributions in each venue;exhibit trends reflecting the cultural dynamics of the eras covered;and have evolved into contemporary forms on social media. Copyright © 2021, The Authors. All rights reserved.

关键词： History

The incel lexicon: Deciphering the emergent cryptolect of a global misogynistic community

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Gothard, Kelly Dewhurst, David Rushing Minot, Joshua R. Adams, Jane Lydia 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 The University of Vermont BurlingtonVT05401 United States Charles River Analytics 625 Mount Auburn Street CambridgeMA02138 United States Department of Mathematics & Statistics University of Vermont BurlingtonVT05401 United States Department of Computer Science University of Vermont BurlingtonVT05401 United States

Evolving out of a gender-neutral framing of an involuntary celibate identity, the concept of 'incels' has come to refer to an online community of men who bear antipathy towards themselves, women, and society-at-large for their perceived inability to find and maintain sexual relationships. By exploring incel language use on Reddit, a global online message board, we contextualize the incel community's online expressions of misogyny and real-world acts of violence perpetrated against women. After assembling around three million comments from incel-themed Reddit channels, we analyze the temporal dynamics of a data driven rank ordering of the glossary of phrases belonging to an emergent incel lexicon. Our study reveals the generation and normalization of an extensive coded misogynist vocabulary in service of the group's identity. Copyright © 2021, The Authors. All rights reserved.

关键词：

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

学校读者我要写书评

暂无评论

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

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

学校读者我要写书评

暂无评论

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