10 citations · 16 across the 5 of their papers we have counts for
6 papers · 1 filter
Long-term word frequency dynamics derived from Twitter are corrupted: A bespoke approach to detecting and removing pathologies in ensembles of time series
P. S. Dodds, J. R. Minot, M. V. Arnold +5
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 sy…
The sociospatial factors of death: Analyzing effects of geospatially-distributed variables in a Bayesian mortality model for Hong Kong
Thayer Alshaabi, David Rushing Dewhurst, James P. Bagrow +2
Human mortality is in part a function of multiple socioeconomic factors that differ both spatially and temporally. Adjusting for other covariates, the human lifespan is positively…
Divergent modes of online collective attention to the COVID-19 pandemic are associated with future caseload variance
David Rushing Dewhurst, Thayer Alshaabi, Michael V. Arnold +3
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…
How the world's collective attention is being paid to a pandemic: COVID-19 related n-gram time series for 24 languages on Twitter
T. Alshaabi, J. R. Minot, M. V. Arnold +6
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 crucia…
Noncooperative dynamics in election interference
David Rushing Dewhurst, Christopher M. Danforth, Peter Sheridan Dodds
Foreign power interference in domestic elections is an existential threat to societies. Manifested through myriad methods from war to words, such interference is a timely example o…
The shocklet transform: A decomposition method for the identification of local, mechanism-driven dynamics in sociotechnical time series
David Rushing Dewhurst, Thayer Alshaabi, Dilan Kiley +4
We introduce a qualitative, shape-based, timescale-independent time-domain transform used to extract local dynamics from sociotechnical time series---termed the Discrete Shocklet T…