10 citations · 31 across the 6 of their papers we have counts for
9 papers
Say Their Names: Resurgence in the collective attention toward Black victims of fatal police violence following the death of George Floyd
Henry H. Wu, Ryan J. Gallagher, Thayer Alshaabi +7
The murder of George Floyd by police in May 2020 sparked international protests and renewed attention in the Black Lives Matter movement. Here, we characterize ways in which the on…
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…
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…
Interpretable bias mitigation for textual data: Reducing gender bias in patient notes while maintaining classification performance
Joshua R. Minot, Nicholas Cheney, Marc Maier +3
Medical systems in general, and patient treatment decisions and outcomes in particular, are affected by bias based on gender and other demographic elements. As language models are…
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…