7 citations · 17 across the 4 of their papers we have counts for
4 papers · 1 filter
Quantifying language changes surrounding mental health on Twitter
Anne Marie Stupinski, Thayer Alshaabi, Michael V. Arnold +5
Mental health challenges are thought to afflict around 10% of the global population each year, with many going untreated due to stigma and limited access to services. Here, we expl…
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…
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…