Online Appendices:

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

Abstract:

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.

Figure 4 from the paper (updated daily through to the end of 2020):

 

A. Chronopathic equivalency heat map: Each cell represents the ‘story distance’ for a given month and a given number of δ days before. We measure story distance as the median rank-turbulence divergence (RTD) between the Zipf distributions of 1-grams used in Trump-matching tweets for each day of a month and δ days before (we use RTD parameter α=1/4). Lighter colors on the perceptually uniform color map correspond to higher levels of story turnover. Numbers indicate the slowest five months for each value of δ. After the story turbulence of the 2016 election year and especially the first year of Trump’s presidency, there has been a general slowing down in story turnover at all time scales (the ‘plot thickens’). By July 2020, the COVID-19 pandemic has induced record slowing down of story turnover around Trump at time scales up to 91 days, punctuated by the Black Lives Matter protests in response to George Floyd’s murder. B. Using an example anchor of April 2020 and δ=14 days (white square in panel A), a plot of chronopathic equivalent values of δ across time. During Trump’s presidency, the same story turnover occurred as fast as every 1.8 days in September 2017 and 1.7 days in October 2020. Because story turbulence is nonlinear, using a different anchor month and δ (i.e., selecting a different cell in the heatmap) potentially gives a different chronopathic equivalency plot. C. Anchor of 56 days in May 2020. D.: Anchor of 182 days in August 2020. As for Fig. 3, shading and lines give guides for years and quarters.