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      Postmortem memory of public figures in news and social media

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          Significance

          Who is remembered by society after they die? Although scholars as well as the broader public have speculated about this question since ancient times, we still lack a detailed understanding of the processes at work when a public figure dies and their media image solidifies and is committed to the collective memory. To close this gap, we leverage a comprehensive 5-y dataset of online news and social media posts with millions of documents per day. By tracking mentions of thousands of public figures during the year following their death, we reveal and model the prototypical patterns and biographic correlates of postmortem media attention, as well as systematic differences in how the news vs. social media remember deceased public figures.

          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.

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          Most cited references79

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          Silhouettes: A graphical aid to the interpretation and validation of cluster analysis

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            Experimental study of inequality and unpredictability in an artificial cultural market.

            Hit songs, books, and movies are many times more successful than average, suggesting that "the best" alternatives are qualitatively different from "the rest"; yet experts routinely fail to predict which products will succeed. We investigated this paradox experimentally, by creating an artificial "music market" in which 14,341 participants downloaded previously unknown songs either with or without knowledge of previous participants' choices. Increasing the strength of social influence increased both inequality and unpredictability of success. Success was also only partly determined by quality: The best songs rarely did poorly, and the worst rarely did well, but any other result was possible.
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              Fake news on Twitter during the 2016 U.S. presidential election

              The spread of fake news on social media became a public concern in the United States after the 2016 presidential election. We examined exposure to and sharing of fake news by registered voters on Twitter and found that engagement with fake news sources was extremely concentrated. Only 1% of individuals accounted for 80% of fake news source exposures, and 0.1% accounted for nearly 80% of fake news sources shared. Individuals most likely to engage with fake news sources were conservative leaning, older, and highly engaged with political news. A cluster of fake news sources shared overlapping audiences on the extreme right, but for people across the political spectrum, most political news exposure still came from mainstream media outlets.
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                Author and article information

                Journal
                Proc Natl Acad Sci U S A
                Proc Natl Acad Sci U S A
                pnas
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                21 September 2021
                15 September 2021
                15 September 2021
                : 118
                : 38
                : e2106152118
                Affiliations
                [1] aSchool of Computer and Communication Sciences, Ecole Polytechnique Fédérale de Lausanne , 1015 Lausanne, Switzerland;
                [2] bDepartment of Computer Science, Stanford University , Stanford, CA 94305;
                [3] cDepartment of Linguistics, Stanford University , Stanford, CA 94305
                Author notes
                1To whom correspondence may be addressed. Email: robert.west@ 123456epfl.ch .

                Edited by Henry L. Roediger III, Washington University in St. Louis, St. Louis, MO, and approved June 24, 2021 (received for review April 1, 2021)

                Author contributions: R.W., J.L., and C.P. designed research; R.W. performed research; R.W. analyzed data; and R.W., J.L., and C.P. wrote the paper.

                Author information
                http://orcid.org/0000-0002-3984-1232
                http://orcid.org/0000-0002-5411-923X
                Article
                202106152
                10.1073/pnas.2106152118
                8463883
                34526401
                6fe5ebcd-d6f6-4fe9-a2b8-08d7629c5868
                Copyright © 2021 the Author(s). Published by PNAS.

                This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY).

                History
                : 24 June 2021
                Page count
                Pages: 10
                Funding
                Funded by: Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (SNF) 501100001711
                Award ID: 200021_185043
                Award Recipient : Robert West
                Funded by: National Science Foundation (NSF) 100000001
                Award ID: OAC-1835598
                Award Recipient : Jure Leskovec
                Funded by: National Science Foundation (NSF) 100000001
                Award ID: OAC-1934578
                Award Recipient : Jure Leskovec
                Funded by: National Science Foundation (NSF) 100000001
                Award ID: CCF-1918940
                Award Recipient : Jure Leskovec
                Funded by: National Science Foundation (NSF) 100000001
                Award ID: IIS-2030477
                Award Recipient : Jure Leskovec
                Funded by: École Polytechnique Fédérale de Lausanne (EPFL) 501100001703
                Award ID: CROSS 2019
                Award Recipient : Robert West
                Categories
                411
                432
                Physical Sciences
                Computer Sciences
                Social Sciences
                Social Sciences

                computational social science,collective memory,news and social media analysis,forgetting

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