Postmortem memory of public figures in news and social media

Abstract

Deceased public figures are often said to live on in collective memory. We quantify this phenomenon by tracking mentions of 2,362 public figures in English-language online news and social media (Twitter) 1 y before and after death. We measure the sharp spike and rapid decay of attention following death and model collective memory as a composition of communicative and cultural memory. Clustering reveals four patterns of postmortem memory, and regression analysis shows that boosts in media attention are largest for premortem popular anglophones who died a young, unnatural death; that long-term boosts are smallest for leaders and largest for artists; and that, while both the news and Twitter are triggered by young and unnatural deaths, the news additionally curates collective memory when old persons or leaders die. Overall, we illuminate the age-old question of who is remembered by society, and the distinct roles of news and social media in collective memory formation.

A two-phase model of collective memory decay with a dynamical switching point

“…Model fitting. We performed model fitting for each normalized time-series data of page views S(t) with the following four nonlinear models that do not assume a regime shift: bi-exponential C 1 e −αt + C 2 e −βt 3 , stretched exponential e −(t/α) γ t δ 19 , shifted power-law C 1 t −α + C 2 20 , and the proposed model C 1 e −βt + C 2 t −α . In model fitting, we added a constant value ǫ to each individual time series, where ǫ was the minimum nonzero value across all individual time series.…”

“…In model fitting, we added a constant value ǫ to each individual time series, where ǫ was the minimum nonzero value across all individual time series. Then, we took base-10 logarithms of the empirical data and conducted parameter fitting of each model formula to the data in a log-log space using a nonlinear least-squares method, following the method by West et al 20 . Figure 3 shows examples of model fitting.…”

“…We selected the following five categories of significant events for analysis: earthquakes, deaths of notable persons, aviation accidents, mass murder incidents, and terrorist attacks. These events were also used in previous collective memory studies 10,11,20,21,30 . For the events in these categories, the date and location of the event are precise, which allows for the collection of unambiguous time series data.…”

“…They successfully depict the dynamics that decay quickly in the beginning and slowly in the latter using the stretched exponential function. West et al 20 studied the daily collective user behavior of Twitter and news sites on the news about the deaths of celebrities between 2009 and 2014. They showed that the mention frequency can be modeled by a shifted power-law function C 1 t −α + C 2 with their exponents are α = 1.34 and α = 1.54 for news and Twitter, respectively.…”

United States Politicians' Tone Became More Negative with 2016 Primary Campaigns

“…The analyzed quotes were obtained from Quotebank (Vaucher et al 2021a), a publicly available (Vaucher et al 2021b) corpus of 235 million unique speaker-attributed quotes extracted from 127 million English news articles published between September 2008 and April 2020, provided by the large-scale online media aggregation service Spinn3r.com. While Spinn3r.com collects and supplies content from a comprehensive set of news domains (West, Leskovec, and Potts 2021), it also includes much content beyond news alone, including "social media, weblogs, news, video, and live web content" (Spinn3r 2017). Therefore, Quotebank was extracted from a filtered data set consisting only of content from a set of about 17,000 online news domains, defined as the set of domains appearing at least once in the large News on the Web corpus (News on the Web Corpus), which has been collecting large numbers of news articles from Google News 10.…”