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

P. S. Dodds, J. R. Minot, M. V. Arnold, T. Alshaabi, J. L. Adams, A. J. Reagan, and C. M. Danforth


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 onwards.

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 year for Trump, and that story generation slowed dramatically during the COVID-19 pandemic in 2020.

Trump story turnover for 2 months during the COVID-19 pandemic was on par with that of 3 days in September 2017.

Our approach may be applied to any well discussed phenomenon, helping to enable computational journalism, computational history, and computational biography.

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