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 2016–2021. 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.
Highlights
What happened in the world last week? What about a year ago? As individuals, it can be difficult for us to freely recall and order in time—let alone make sense of—events that have occurred at scopes running from personal and day-to-day to global and historic [1,2,3,4,5,6,7,8,9,10]
Computational timeline reconstruction of stories surrounding Trump including the "Focalevents" package from Ryan Gallagher which can be found on GitHub: https:// github.com/ryanjgallagher/focalevents Given that our results are statistical estimates of word frequencies found in a 10% random subset of "trump" messages, drawn from the Decahose stream provided by Twitter, qualitatively similar results will be found using any sufficiently large collection of "trump" messages
Because we have shown that the narratively dominant n-grams our method find are historically sensible, we are able to defensibly quantify narrative control, story turnover, and collective chronopathy in the context of Trump
Summary
What happened in the world last week? What about a year ago? As individuals, it can be difficult for us to freely recall and order in time—let alone make sense of—events that have occurred at scopes running from personal and day-to-day to global and historic [1,2,3,4,5,6,7,8,9,10]. What happened in the world last week? Computational timeline reconstruction of stories surrounding Trump including the "Focalevents" package from Ryan Gallagher which can be found on GitHub: https:// github.com/ryanjgallagher/focalevents Given that our results are statistical estimates of word frequencies found in a 10% random subset of "trump" messages, drawn from the Decahose stream provided by Twitter, qualitatively similar results will be found using any sufficiently large collection of "trump" messages. Messages authored by President Trump can be found at the Trump Twitter Archive can be used as a source for the former President’s messages at https://www. Messages authored by President Trump can be found at the Trump Twitter Archive can be used as a source for the former President’s messages at https://www. thetrumparchive.com
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