围绕特朗普的故事的计算时间轴重建：故事turbulence，叙事控制和集体时间病
Computational timeline reconstruction of the stories surrounding Trump: Story turbulence, narrative control, and collective chronopathy
摘要：
测量特定类型，时间顺序，多样性，以及围绕任何给定主题的故事的周转率，对于发展该主题的历史影响的完整估计是必不可少的。在这里，我们使用Twitter作为分布式新闻和意见聚合源，以识别和跟踪围绕美国第45任总统唐纳德·特朗普的主导日规模故事的动态。使用一个包含约200亿1克的数据集，我们首先将每天1克和2克的使用频率与一年前的使用频率进行比较，以创建2016年至2020年特朗普故事的日和周时间表。我们衡量特朗普的叙事控制力，即故事在多大程度上是关于特朗普的或由特朗普提出的。然后，我们量化故事动荡和集体时间错乱-人口的故事似乎随着时间的推移而变化的速度。我们发现，2017年是特朗普最动荡的一年。于二零二零年，新冠肺炎疫情的前两波主要疫情期间，故事生成大幅放缓，首先是乔治·弗洛伊德被谋杀后的Black Lives Matter抗议活动，随后是二零二零年美国总统大选前后的事件，包括二零二一年六天前美国国会大厦的冲击，迅速恢复。于2019冠状病毒病疫情期间，特朗普故事两个月的成交量与二零一七年九月的三天持平。我们的方法可以应用于任何讨论的现象，并有可能使计算方面的新闻，历史和传记。
英文摘要：
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 through 2020. 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
