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Probability-turbulence divergence: A tunable allotaxonometric instrument for comparing heavy-tailed categorical distributions

Another new preprint for @compstorylab: “Probability-turbulence divergence: A tunable allotaxonometric instrument for comparing heavy-tailed categorical distributions” https://arxiv.org/abs/2008.13078 Allotaxonometry is the detail-rich comparison of two complex systems, or one evolving complex system with itself at different time points. See our foundational paper—where we introduce rank-turbulence divergence—for the full story ...

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

New preprint from @compstorylab: “Long-term word frequency dynamics derived from Twitter are corrupted: A bespoke approach to detecting and removing pathologies in ensembles of time series” https://arxiv.org/abs/2008.11305. In short: “Run-roh”. We find we have to say this repeatedly: Accurate measurement is fundamental to understanding anything. And anything includes social phenomena. ...

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

New preprint: “Computational timeline reconstruction of the stories surrounding Trump: Story turbulence, narrative control, and collective chronopathy.” Some questions to ask yourself and others: What happened in the world over the last two weeks? What about this time last year? Two years ago? And what order did the major events happen in? ...

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Local information sources received the most attention from Puerto Ricans during the aftermath of Hurricane Maria

Short thread for a new @compstorylab paper in for review, led by @dbemerydt: “Local information sources received the most attention from Puerto Ricans during the aftermath of Hurricane Maria” Preprint: https://arxiv.org/abs/2007.09124 Collaboration with @MeredithNiles1. We perform a case study of communication generated by Hurricane Maria's devastation across Puerto Rico To do so, we apply methods produced in the fields of network science, information theory, and natural language processing in the context of natural disasters. ...

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The sociospatial factors of death: Analyzing effects of geospatially-distributed variables in a Bayesian mortality model for Hong Kong

"The sociospatial factors of death: Analyzing effects of geospatially-distributed variables in a Bayesian mortality model for Hong Kong” New preprint from our group https://arxiv.org/abs/2006.08527 At the individual level, lifespan is positively associated with wealth. But at the population scale, what role does geography play? ...

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Ratioing the President: An exploration of public engagement with Obama and Trump on Twitter

Thread for a new paper of ours on the arXiv: “Ratioing the President: An exploration of public engagement with Obama and Trump on Twitter” https://arxiv.org/abs/2006.03526  J. R. Minot, M. V. Arnold, T. Alshaabi, C. M. Danforth, P. S. Dodds. We explore the dynamics of how Twitter users have responded to tweets made by Obama and Trump from their main accounts, @BarackObama and @realDonaldTrump. ...

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

“Divergent modes of online collective attention to the COVID-19 pandemic are associated with future caseload variance” New preprint from our group led by @d_r_dewhurst https://arxiv.org/abs/2004.03516 Across 24 languages, we find two distinct dynamic regimes: The first characterizing the rise and subsequent collapse in collective attention to the initial Coronavirus outbreak in late January, and the second representing March COVID-19-related discourse. ...

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Hurricanes and hashtags: Characterizing online collective attention for natural disasters

“Hurricanes & hashtags: Characterizing online collective attention for natural disasters” New preprint on arXiv: https://arxiv.org/abs/2003.14291 Using hurricane name mentions as a proxy for awareness, we find that attention varies widely even among storms causing comparable deaths and damage. The worst storms of the 2010s, Harvey and Maria, generated the most attention and were remembered the longest, respectively. ...

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