Estimating Name Recognition of Public Figures from the Web

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

In this paper, we propose a method for estimating the name recognition of public figures, that is, the proportion of the population that recognizes the individual. It is typically measured through surveys. The proposed method estimates changes in name recognition relative to the previous survey results, enabling us to infer current values without conducting repeated surveys. It uses two types of information: (1) the number of recent page views of the individual’s Wikipedia page, and (2) the topics of recent news articles about the individual. The former serves as a proxy for the scale of events that made people newly aware of the individual, while the latter captures the types of the events. We conducted an experiment on a dataset of 967 individuals. The result shows that the combination of these two types of information improves estimation accuracy.