  Measuring the specific kind, temporal ordering, diversity, and turnover rate
of stories surrounding any given subject is essential to developing a complete
reckoning of that subject's historical impact. Here, we use Twitter as a
distributed news and opinion aggregation source to identify and track the
dynamics of the dominant day-scale stories around Donald Trump, the 45th
President of the United States. Working with a data set comprising around 20
billion 1-grams, we first compare each day's 1-gram and 2-gram usage
frequencies to those of a year before, to create day- and week-scale timelines
for Trump stories for 2016 through 2020. We measure Trump's narrative control,
the extent to which stories have been about Trump or put forward by Trump. We
then quantify story turbulence and collective chronopathy -- the rate at which
a population's stories for a subject seem to change over time. We show that
2017 was the most turbulent overall year for Trump. In 2020, story generation
slowed dramatically during the first two major waves of the COVID-19 pandemic,
with rapid turnover returning first with the Black Lives Matter protests
following George Floyd's murder and then later by events leading up to and
following the 2020 US presidential election, including the storming of the US
Capitol six days into 2021. Trump story turnover for 2 months during the
COVID-19 pandemic was on par with that of 3 days in September 2017. Our methods
may be applied to any well-discussed phenomenon, and have potential to enable
the computational aspects of journalism, history, and biography.
