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 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.

Toward love and sex narrative generation using a noun conceptual dictionary

At “The International Congress on Love & Sex with Robots,” love and sex issues related to robots have been discussed. This discussion of robots has applications in nursing care and other. Love and sex are also important themes for narratives. We develop a system to generate stories, and consider a robot that tells stories as one of the applications of story generation. The purpose is to present a prototyping system that generates a new narrative expression based on the theme of “love and sex” by exchanging the concept of character in the input narrative expression with a new concept using our noun concept dictionary. We call the method of collecting nouns based on a certain theme and embedding them in a story to give the story a certain atmosphere “colouring.” This paper is to develop a prototype of a system that uses “colouring” to give a certain atmosphere to a story. We create a prototype and study the issues of the system. In the future, this prototype will serve as a stepping stone to a system that generates narratives based on specific themes. Eventually, we will study the use of robotic interactive psychotherapy, in which the robot converses with humans.

Automatic Story Generation

Computational generation of stories is a subfield of computational creativity where artificial intelligence and psychology intersect to teach computers how to mimic humans’ creativity. It helps generate many stories with minimum effort and customize the stories for the users’ education and entertainment needs. Although the automatic generation of stories started to receive attention many decades ago, advances in this field to date are less than expected and suffer from many limitations. This survey presents an extensive study of research in the area of non-interactive textual story generation, as well as covering resources, corpora, and evaluation methods that have been used in those studies. It also shed light on factors of story interestingness.