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Measuring the specific kind, temporal ordering, diversity, and turnover rateof stories surrounding any given subject is essential to developing a completereckoning of that subject's historical impact. Here, we use Twitter as adistributed news and opinion aggregation source to identify and track thedynamics of the dominant day-scale stories around Donald Trump, the 45thPresident of the United States. Working with a data set comprising around 20billion 1-grams, we first compare each day's 1-gram and 2-gram usagefrequencies to those of a year before, to create day- and week-scale timelinesfor 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. Wethen quantify story turbulence and collective chronopathy -- the rate at whicha population's stories for a subject seem to change over time. We show that2017 was the most turbulent overall year for Trump. In 2020, story generationslowed dramatically during the first two major waves of the COVID-19 pandemic,with rapid turnover returning first with the Black Lives Matter protestsfollowing George Floyd's murder and then later by events leading up to andfollowing the 2020 US presidential election, including the storming of the USCapitol six days into 2021. Trump story turnover for 2 months during theCOVID-19 pandemic was on par with that of 3 days in September 2017. Our methodsmay be applied to any well-discussed phenomenon, and have potential to enablethe computational aspects of journalism, history, and biography.

Computational timeline reconstruction of the stories surrounding Trump: Story turbulence, narrative control, and collective chronopathy