63 citations · 259 across the 20 of their papers we have counts for
20 papers
The incel lexicon: Deciphering the emergent cryptolect of a global misogynistic community
Kelly Gothard, David Rushing Dewhurst, Joshua R. Minot +3
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 the…
Structural time series grammar over variable blocks
David Rushing Dewhurst
A structural time series model additively decomposes into generative, semantically-meaningful components, each of which depends on a vector of parameters. We demonstrate that consi…
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…
Storywrangler: A massive exploratorium for sociolinguistic, cultural, socioeconomic, and political timelines using Twitter
Thayer Alshaabi, Jane L. Adams, Michael V. Arnold +5
In real-time, social media data strongly imprints world events, popular culture, and day-to-day conversations by millions of ordinary people at a scale that is scarcely conventiona…
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…