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June 2020

Demographics behind 1,000 names of COVID-19 victims published by NYT

Results

About

Gender

Age

18-39

40-59

60+

Race

White American

African American

Asian American

Native American

Two or more races

Hispanic origin

Hispanic

Non-Hispanic

Ethnicity

British

Hispanic

French

Italian

African

Jew

East European

Germanic

East Asian

Indian

Nordic

Japanese

Arab

New York Times published names of 1,000 people (out of more than 100,000) who died from COVID-19 in US. This is a sad publication featuring names, age, short bio and state of residence of 1,000 people whose names were compiled from numerous publications across the country. Even sadder is the fact that this is less than 1% of all victims. The paper version of newspaper dedicated several full pages to accommodate the names of the victims.

While it is a tragic tribute to COVID-19 victims, many researches fighting COVID-19 are interested in case by case individual data because it enables to use this data in machine learning algorithms designed to fight COVID-19 in one or another way. Access to case data provides a variety of possible vital opportunities:

Get demographic insights to see the most affected demographics

Analyze location data to see the most affected geographies

Analyze medical data (conditions, treatment, test results, x-rays, etc) to find out contributing factors

And such case data is almost absent in public domain leaving a large gap in opportunity to leverage the power of citizen science and large number of data researchers globally.

Goal of the research

NYT 1,000 names is not a solution to this problem of course. It's a relatively small and incomplete data set of cases (e.g. it doesn't contain useful medical information). However it contains names so we decided to conduct a case study of measuring COVID-19 victims' demographics as a proof-of-concept of how Demografy can be used as one step in using AI to combat COVID-19 - getting richer dempographic data for each case.

For the present case study, we've used online version of New York Times publication containing data set of 1,000 names of US COVID-19 victims. Unfortunately web page displaying the data uses canvas HTML5 element so data can't be copied directly via trivial automated means. As a result we compiled data set manually.

Data for each person contains the following information:

Full name

Age

State

City or county (optional)

Short bio

After manually collecting all the data in tabular format, we cleaned the data using a program written for this purpose. Program normalizes names by removing middle names, initials and abbreviations such as "Jr." or "Sr." leaving first and last name separately. Then Demografy was used to classify demographic indicators. Demografy uses supervised machine learning algorithms to classify gender, age group, race, Hispanic origin and ethnicity from first and last names.

More details about methodology is availbele by request.

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March 2018

How we measured Twitter demographics using AI

Results

About

Pew Research, comScore

Source

Demografy

Surveys and verified panel data

Method

Demografy's AI

Gender

18-39

40-59

60+

We are proud to publish our first case study. In this case study we used Demografy to measure age range and gender of US Twitter users and compared our results with two separate studies on Twitter demographics published by Pew Research and comScore. We also made a brief overview of existing technologies of measuring demographics of website audiences.

Goal of the research

One of the reasons behind the research is assessment of Demografy’s accuracy. However before conducting this research we have already tested accuracy of Demografy by comparing its results with hundreds of thousands of publicly available self-reported records of real people.

So there are three key aspects of the research:

Explore and compare available approaches to measuring Internet demographics.

Benchmark our own performance against known and well established solutions.

Implement proof of concept of a brand new demographics measuring method that eliminates disadvantages of traditional approaches.

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July 2018

Measuring gender bias in films: comparing our results with Google's

Results

About

Female on-screen time

Google

Source

Demografy

Face and gender recognition in video tracks

Method

Gender detection in cast names

By MPAA rating

PG-13

By Oscar-winning vs all

Oscar-winning

All movies

By genre

Horror

Romance

Comedy

Sci-Fi

Drama

Biography

Action

Crime

Inspired by Google/GD-IQ 2017 study on gender bias in 300 top grossing 2014–2016 US movies, we used Demografy to measure female on-screen time in the same movies by analyzing names of main character cast. Though both studies use different technologies and measure slightly different indicators we got very close results and decided to publish them.

Goal of the research

Like in our previous study of measuring Twitter demographics, we were challenged to use Demografy technology on a real world task with results that can be cross-verified against reputed source. Google’s 2017 study was a perfect choice for this purpose.

Difference in measured indicators:

First of all, we’d like to highlight difference between the two studies.

Google measured share of female on-screen time by analyzing video frames with detected human faces.

Demografy measured share of female cast by analyzing names of top 10 characters in cast assuming that first 10 actors in cast represent main characters in the movie.

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September 2018

Measuring racial and gender diversity in films using AI

Results

About

Race/ethnicity and gender composition of film cast and crew

USC Annenberg Inclusion Initiative

Source

Demografy

Manual data compilation from open sources

Method

Race/ethnicity and gender detection in names using ML

By race/ethnicity

Cast

White American

African American

Hispanic

Asian American

Native American

Middle Eastern

Other

By race/ethnicity

Directors

Non-Black and non-Asian

Asian American

African American

By gender

Cast

Producers

Writers

Directors

As a development of our previous case study of measuring gender bias in films, we decided to measure more demographic indicators using our technology. In this case study we measured gender composition of different roles in film industry as well as racial and ethnic composition of actors in top grossing US movies. We compared our results with USC Annenberg’s 2015 report since it contains a comprehensive and detailed data on measured demographic compositions.

About the Annenberg Inclusion Initiative

AII is the leading think tank in the world studying diversity and inclusion in entertainment through original research and sponsored projects. Beyond research, the Annenberg Inclusion Initiative develops targeted, research-based solutions to tackle inequality.

What data is measured

We measured gender and racial demographics for the following data:

Gender of directors, producers, writers and actors for 2015 top grossing US movies

Race and ethnicity of actors and directors in 2015 top grossing US movies

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