The annotator assigned 36 names to the S category and 27 names to the B category; the remaining 37 were given the ambiguous N classification. Of the names assigned to the S category by the human annotator, 29 had been annotated as suppressed by our algorithm, and 7 as elevated, so the correspondence between the annotator and our algorithm was 81%. Of the names assigned to the B category, 25 were annotated as elevated by our algorithm, and only 2 as suppressed, so the correspondence was 93%. Taken together, the conclusions of a scholarly annotator researching one name at a time closely matched those of our automated approach. These findings confirm that our computational method provides an effective strategy for rapidly identifying likely victims of censorship given a large pool of possibilities. III.10. Epidemics Disease epidemics have a significant impact on the surrounding culture (Fig. $18 A-C). It was recently shown that during seasonal influenza epidemics, users of Google are more likely to engage in influenza- related searches, and that this signature of influenza epidemics corresponds well with the results of CDC surveillance (Ref S16). We therefore reasoned that culturomic approaches might be used to track historical epidemics. These could help complement historical medical records, which are often woefully incomplete. We examined timelines for 4 diseases: influenza (main text), cholera, HIV, and poliomyelitis. In the case of influenza, peaks in cultural interest showed excellent correspondence with known historical epidemics (the Russian Flu of 1890, leading to 1M deaths, the Spanish Flu of 1918, leading to 20-100M deaths; and the Asian Flu of 1957, leading to 1.5M deaths). Similar results were observed for cholera and HIV. However, results for polio were mixed. The US epidemic of 1916 is clearly observed, but the 1951-55 epidemic is harder to pinpoint: the observed peak is much broader, starting in the 30s and ending in the 60s. This is likely due to incre