Cargando…

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

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 t...

Descripción completa

Detalles Bibliográficos
Autores principales: Dodds, Peter Sheridan, Minot, Joshua R., Arnold, Michael V., Alshaabi, Thayer, Adams, Jane Lydia, Reagan, Andrew J., Danforth, Christopher M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8654215/
https://www.ncbi.nlm.nih.gov/pubmed/34879105
http://dx.doi.org/10.1371/journal.pone.0260592
_version_ 1784611818117791744
author Dodds, Peter Sheridan
Minot, Joshua R.
Arnold, Michael V.
Alshaabi, Thayer
Adams, Jane Lydia
Reagan, Andrew J.
Danforth, Christopher M.
author_facet Dodds, Peter Sheridan
Minot, Joshua R.
Arnold, Michael V.
Alshaabi, Thayer
Adams, Jane Lydia
Reagan, Andrew J.
Danforth, Christopher M.
author_sort Dodds, Peter Sheridan
collection PubMed
description 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.
format Online
Article
Text
id pubmed-8654215
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-86542152021-12-09 Computational timeline reconstruction of the stories surrounding Trump: Story turbulence, narrative control, and collective chronopathy Dodds, Peter Sheridan Minot, Joshua R. Arnold, Michael V. Alshaabi, Thayer Adams, Jane Lydia Reagan, Andrew J. Danforth, Christopher M. PLoS One Research Article 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. Public Library of Science 2021-12-08 /pmc/articles/PMC8654215/ /pubmed/34879105 http://dx.doi.org/10.1371/journal.pone.0260592 Text en © 2021 Dodds et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Dodds, Peter Sheridan
Minot, Joshua R.
Arnold, Michael V.
Alshaabi, Thayer
Adams, Jane Lydia
Reagan, Andrew J.
Danforth, Christopher M.
Computational timeline reconstruction of the stories surrounding Trump: Story turbulence, narrative control, and collective chronopathy
title Computational timeline reconstruction of the stories surrounding Trump: Story turbulence, narrative control, and collective chronopathy
title_full Computational timeline reconstruction of the stories surrounding Trump: Story turbulence, narrative control, and collective chronopathy
title_fullStr Computational timeline reconstruction of the stories surrounding Trump: Story turbulence, narrative control, and collective chronopathy
title_full_unstemmed Computational timeline reconstruction of the stories surrounding Trump: Story turbulence, narrative control, and collective chronopathy
title_short Computational timeline reconstruction of the stories surrounding Trump: Story turbulence, narrative control, and collective chronopathy
title_sort computational timeline reconstruction of the stories surrounding trump: story turbulence, narrative control, and collective chronopathy
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8654215/
https://www.ncbi.nlm.nih.gov/pubmed/34879105
http://dx.doi.org/10.1371/journal.pone.0260592
work_keys_str_mv AT doddspetersheridan computationaltimelinereconstructionofthestoriessurroundingtrumpstoryturbulencenarrativecontrolandcollectivechronopathy
AT minotjoshuar computationaltimelinereconstructionofthestoriessurroundingtrumpstoryturbulencenarrativecontrolandcollectivechronopathy
AT arnoldmichaelv computationaltimelinereconstructionofthestoriessurroundingtrumpstoryturbulencenarrativecontrolandcollectivechronopathy
AT alshaabithayer computationaltimelinereconstructionofthestoriessurroundingtrumpstoryturbulencenarrativecontrolandcollectivechronopathy
AT adamsjanelydia computationaltimelinereconstructionofthestoriessurroundingtrumpstoryturbulencenarrativecontrolandcollectivechronopathy
AT reaganandrewj computationaltimelinereconstructionofthestoriessurroundingtrumpstoryturbulencenarrativecontrolandcollectivechronopathy
AT danforthchristopherm computationaltimelinereconstructionofthestoriessurroundingtrumpstoryturbulencenarrativecontrolandcollectivechronopathy