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1.84 MB

Extraction Summary

4
People
4
Organizations
2
Locations
7
Events
1
Relationships
4
Quotes

Document Information

Type: Scientific report / academic paper (supplementary material)
File Size: 1.84 MB
Summary

This document appears to be a page from a scientific paper or supplementary report discussing 'culturomic' approaches to tracking historical epidemics and censorship. It details a comparison between human annotators and algorithms in identifying censorship, and then analyzes historical disease outbreaks (Influenza, Cholera, Polio) using cultural interest data (likely Google Books data). The document bears a House Oversight Bates stamp, suggesting it was part of a document production for a congressional investigation, though the text itself is purely academic.

People (4)

Name Role Context
Franklin Delano Roosevelt Historical Figure
Mentioned regarding increased interest in polio following his election in 1932.
Salk Scientist/Researcher
Mentioned regarding the development of the polio vaccine in 1952.
Sabin Scientist/Researcher
Mentioned regarding the development of the oral polio vaccine in 1962.
Annotator Researcher (Unnamed)
Human researcher whose classifications were compared against an algorithm.

Organizations (4)

Name Type Context
Google
Mentioned regarding user search habits during influenza epidemics.
CDC
Centers for Disease Control; referenced regarding surveillance results.
Cambridge World History of Human Diseases
Source for historical epidemic dates.
House Oversight Committee
Implied via Bates stamp 'HOUSE_OVERSIGHT_017037'.

Timeline (7 events)

1890
Russian Flu epidemic
Global
1916
Polio epidemic
US
1918
Spanish Flu epidemic
Global
1932
Election of Franklin Delano Roosevelt
US
1952
Deployment of Salk's polio vaccine
Global
1957
Asian Flu epidemic
Global
1962
Deployment of Sabin's oral vaccine
Global

Locations (2)

Location Context
US
Location of the 1916 polio epidemic.
Region affected by specific cholera epidemics.

Relationships (1)

Annotator Comparison Algorithm
correspondence between the annotator and our algorithm was 81%... and 93%

Key Quotes (4)

"Taken together, the conclusions of a scholarly annotator researching one name at a time closely matched those of our automated approach."
Source
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Quote #1
"These findings confirm that our computational method provides an effective strategy for rapidly identifying likely victims of censorship given a large pool of possibilities."
Source
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Quote #2
"Disease epidemics have a significant impact on the surrounding culture"
Source
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Quote #3
"We therefore reasoned that culturomic approaches might be used to track historical epidemics."
Source
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Quote #4

Full Extracted Text

Complete text extracted from the document (2,947 characters)

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. S18 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 increased interest in polio following the election of Franklin Delano Roosevelt in 1932, as well as the development and deployment of Salk's polio vaccine in 1952 and Sabin's oral version in 1962. These confounding factors highlight the challenge of interpreting timelines of cultural interest: interest may increase in response to an epidemic, but it may also respond to a stricken celebrity or a famous cure.
The dates of important historical epidemics were derived from the Cambridge World History of Human Diseases (1993) 3rd Edition.
For cholera, we retained the time periods which most affected the Western world, according to this resource:
- 1830-35 (Second Cholera Epidemic)
- 1848-52, and 1854 (Third Cholera Epidemic)
- 1866-74 (Fourth Cholera Epidemic)
- 1883-1887 (Fifth Cholera Epidemic)
The first, sixth and seventh cholera epidemics appear not to have caused significant casualties in the Western world.
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