Zhang et al. do an interesting study using natural language processing to measure reputation over time:
This study uses machine-learning techniques and a historical corpus to examine the evolution of artists’ reputations over time. Contrary to popular wisdom, we find that most artists’ reputations peak just before their death, and then start to decline. This decline is strongest for artists who were most popular during their lifetime. We show that artists’ reduced visibility and changes in the public’s aesthetic taste explain much of the posthumous reputation decline. This study highlights how social perception of historical figures can shift and emphasizes the vulnerability of human reputation. Methodologically, the study illustrates an application of natural language processing to measure reputation over time.Abstract
This study explores the longevity of artistic reputation. We empirically examine whether artists are more- or less-venerated after their death. We construct a massive historical corpus spanning 1795 to 2020 and build separate word-embedding models for each five-year period to examine how the reputations of over 3,300 famous artists—including painters, architects, composers, musicians, and writers—evolve after their death. We find that most artists gain their highest reputation right before their death, after which it declines, losing nearly one SD every century. This posthumous decline applies to artists in all domains, includes those who died young or unexpectedly, and contradicts the popular view that artists’ reputations endure. Contrary to the Matthew effect, the reputational decline is the steepest for those who had the highest reputations while alive. Two mechanisms—artists’ reduced visibility and the public’s changing taste—are associated with much of the posthumous reputational decline. This study underscores the fragility of human reputation and shows how the collective memory of artists unfolds over time.