March 2024 Directors Meeting, Division of Student Affairs and Academic Support
20130630 What motivates library crowdsourcing volunteers? [ALA LITA]
1. What motivates library
crowdsourcing volunteers?
Experiences at the California Digital Newspaper
Collection and the
Cambridge Public Library Newspapers Collection
Brian Geiger
Director, Center for Bibliographical Studies and Research
California Digital Newspaper Collection
Frederick Zarndt
Chair, IFLA Newspapers Section
2. Photo held by John Oxley Library, State Library of Queensland. Original from
Courier-mail, Brisbane, Queensland, Australia.
1. crowds, properties of
2.crowdsourcing, short history of
3.crowdsourcing and libraries, especially
digital newspapers collections
4.crowdsourcing, motivations for
5.crowdsourcing, interesting fact
6.crowdsourcing, website traffic
7. crowdsourcing, accuracy of
8.crowdsourcing, economic benefits of
9.crowdsourcing, other benefits of
4. In it he says ...
In 2004 James Surowiecki published ...
The Wisdom of Crowds: Why
the Many Are Smarter Than
the Few and How Collective
Wisdom Shapes Business,
Economies, Societies and
Nations
10. wisdom of crowds
in summary
James Surowiecki, The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective
Wisdom Shapes Business, Economies, Societies and Nations, Anchor Books, New York, 2005.
Diversity
Each person should have private information
even if it's just an eccentric interpretation of the
known facts.
Independence
People's opinions aren't determined by the
opinions of those around them.
Decentralization
People are able to specialize and draw on local
knowledge.
Aggregation
Some mechanism exists for turning private
judgments into a collective decision.
14. • On the date of publication of Jeff Howe’s Wired
magazine article, 1-Jun-2007, Wikipedia did not have
an entry (list) of crowdsourcing projects*.
• On 25-Jan-2010 Wikipedia’s list of crowdsourcing
projects had 35 entries*.
• On 17-Mar -2013 Wikipedia’s list of crowdsourcing
projects had 158 entries+.
* From Internet Archives’ Wayback Machine.
+ Wikipedia contributors, "List of crowdsourcing projects," Wikipedia, The Free Encyclopedia, https://
en.wikipedia.org/wiki/List_of_crowdsourcing_projects (accessed March 17, 2013).
15. Crowdsourcing is the practice of obtaining
needed services, ideas, or content by
soliciting contributions from a large group of
people, and especially from an online
community, rather than from traditional
employees or suppliers. ... [It] is different
from ordinary outsourcing since it is a task or
problem that is outsourced to an undefined
public rather than a specific, named group.
Wikipedia contributors, "Crowdsourcing," Wikipedia, The Free Encyclopedia, http://en.wikipedia.org/wiki/
Crowdsourcing (accessed March 17, 2013)
16. Amazon Mechanical Turk was launched Nov 2005
Alexa global / USA rank of Amazon Mechanical Turk (Jun 2013): 8,242 / 3,060
Alexa reputation (Jun 2013): 1.344
crowdsourcing
17. CAPTCHA is a Completely Automated Public Turing test to tell Computers and Human
Apart
Started as a computer science project at Carnegie Mellon
Each day 200,000,000 recaptcha’s are solved by humans around the world
18. Open Street Map was 1st launched in 2007
Alexa global / Belarus / USA traffic rank of Open Street Map (Jun 2013): 8,853 / 1,373 / 20,114
Alexa reputation (Jun 2013): 28,368
19. Zooniverse was 1st launched July 2007
Alexa global / USA traffic rank of Zooniverse (Jun 2013): 275,960 / 153,853
Alexa reputation (Jun 2013): 502
Registered users (Jun 2013): 848,140
citizen science
20. Kickstarter was launched in 2009
Alexa global / USA traffic rank of Kickstarter (Jun 2013): 798 / 361
Alexa reputation (Jun 2013): 85,591
43,000+ projects successfully funded with more than USD $669,000,000
crowdfunding
22. • Began 2001
• Now in 285 languages
• 40 wikipedia
languages with more
than 100,000 articles
• 112 wikipedia
languages with more
than 10,000 articles
• 400,000,000 unique
visitors per month
• 3,900,000+ articles in English, 1,400,000+ in German, 1,250,000+ in French, 1,050,000 in
Dutch
• 85,000 active contributors / more than 300,000 have edited Wikipedia more than 10 times
• Alexa global / USA traffic rank (Jun 2013): 7 / 8
• Alexa reputation (Jun 2013): 2,102,569
23. Wikipedia contributors, "List of crowdsourcing projects," Wikipedia, The Free Encyclopedia, http://
en.wikipedia.org/wiki/List_of_crowdsourcing_projectss (accessed Jun 2013).
27. Family Search Indexing was 1st launched (beta) 2004
Alexa global / USA traffic rank of FamilySearch (Jun 2013): 4,664 / 1,373
Alexa reputation (Jun 2013): 8,140
28. • Started (beta) 2004
• More than 780,000 worldwide registered volunteers
from ~25 countries index records relevant to family
history
• Approximately 100,000 active volunteers each month
• UI in Chinese, English, German, French, Italian,
Japanese, Korean, Portuguese, and Russian
• Blind double-key entry with arbitration / reconciliation
• More than 1,500,088,741 records indexed (July 2012)
• Accuracy typically > 99.95%
29. Project Gutenberg was 1st launched Dec 1971
Alexa global traffic rank of Project Gutenberg (Jun 2013): 6,709
Alexa reputation (Jun 2013): 31,190
30. • Started Dec 1971
• Worldwide volunteers transcribe or proofread OCR’d
public domain books through Distributed Proofreaders
• 40,000 books completed (July 2012)
• Partner / affiliated projects for Australia, Canada,
Europe, Germany, Luxembourg, Philippines, Runeberg
(Nordic literature), Russia, Taiwan
31. Alexa global / Australia traffic rank of National Library of Australia (June 2013): 15,435 / 411
Trove gets ~77% of all National Library web traffic.
Alexa reputation (Jun 2013): 8,788
32. National Library of
Australia
• Online since 2008
• More than 9,830,000 / 96,000,000 newspaper
pages / articles (May 2013)
• Top text corrector 1,911,000+ lines (May 2013)
• 2,661,140 lines corrected each month (average for
1st 5 months 2013)
• 96,304,337 lines corrected as of May 2013, up from
66,527,535 lines corrected May 2012
• 95,299 / 8,519 registered / active users (May 2013)
33. Alexa global / country traffic rank of National Library of Finland
2,535,854 (31-Oct-2012) / 199 (2-Apr-2012)
34. National Library of
Finland
• Digitalkoot is a project to improve OCR text in
digitized newspapers -- by playing games!
• Digitalkoot is a collaboration between the National
Library and Microtask
• Players correct OCR text by playing Myyräsillassa
(Mole Bridge) or Myyräjahdissa (Mole Hunt)
• National Library has 4,000,000+ digitized pages
• 109,321 registered players (October 2012)
• Since February 2011 8,024,530 micro-tasks have
been completed
35. Alexa global / USA traffic rank of UC Riverside (June 2013): 12,584 / 4,382
CDNC gets ~1.6% of all UC Riverside web traffic.
36. California Digital
Newspaper Collection
• CDNC began digitizing newspapers in 2005 as
part of NDNP
• Newspapers digitized to article-level as well as
to page-level as required by NDNP
• Hosted on Veridian beginning 2009
• Collection size 61,351 issues, 544,474 pages,
6,327,491 articles (May 2013)
37. OCR text correction
• OCR text correction added Aug 2011
• Corrections are done line by line
• 1340 registered / 599 active users (Jun 2013)
• ~1,160,465+ lines of text corrected (Jun 2013)
• ~1.1% of the collection corrected, 98.9% to go!
• Top corrector 379,573 lines > 2x 2nd corrector
39. Cambridge Public Library
Historic Newspaper Collection
• Cambridge Historic Newspapers online since Jan 2012.
• Cambridge Massachusetts Public Library digitized local
newspapers (http://cambridge.dlconsulting.com/)
• Newspapers digitized to article-level
• Collection size 6,346 issues, 59,070 pages, 669,406
articles (May 2013)
• Collection includes 13,099 obituary cards
41. Cognitive surplus
... people are learning to use their free time for creative
activities rather than consumptive ones [such as watching
TV] ...
... the total human cognitive effort in creating all of
Wikipedia in every language is about one hundred million
hours ...
... Americans alone watch two hundred billion hours of TV
every year, or enough time, if it would be devoted to projects
similar to Wikipedia, to create about 2000 of them ...
Clay Shirky. Cognitive surplus: Creativity and generosity in a connected age. Penguin Press. New York. 2010.
44. Motivation
genealogists and family
historians
• National Library of Australia’s 2012 Trove
status report showed that ~50% of Trove users
are family historians
• National Library of New Zealand survey found
that ~50% of PapersPast users are genealogists
• A 2012 Utah Digital Newspapers survey
showed that 72% of its users are genealogists*
PAPERSPAST
*John Herbert and Randy Olsen. “Small town papers: Still delivering the news”. Paper given
at 2012 World Library and Information Congress. Helsinki. August 2012.
45. User survey
• CDNC and Cambridge Public Library published a user
survey in Mar 2013 (it’s still online)
• 604 / 32 responses
• surveys are (mostly) identical except for organization
name
Survey is on home pages at http://cdnc.ucr.edu and http://cambridge.dlconsulting.com/
50. “I enjoy the correction - it’s a great way to learn more about
past history and things of interest whilst doing a ‘service to
the community’ by correcting text for the benefit of others.”
“I have recently retired from IT and thought that I could be
of some assistance to the project. It benefits me and other
people. It helps with family research.”
From Rose Holley in “Many Hands Make Light Work.” National Library of Australia March 2009.
Motivation
Trove users’ report
51. “I am interested in all kinds of history. I have pursued genealogy
as a hobby for many years. I correct text at CDNC because I see
it as a constructive way to contribute to a worthwhile project.
Because I am interested in history, I enjoy it.”
Wesley, California
Personal communications with CDNC text correctors.
Motivation
CDNC users’ report
52. “I only correct the text on articles of local interest - nothing at
state, national or international level, no advertisements, etc.
The objective is to be able to help researchers to locate local
people, places, organizations and events using the on-line
search at CDNC. I correct local news & gossip, personal items,
real estate transactions, superior court proceedings, county and
local board of supervisors meetings, obituaries, birth notices,
marriages, yachting news, etc.”
Ann, California
Personal communications with CDNC text correctors.
Motivation
CDNC users’ report
53. “I am correcting text for the Coronado Tent City Program for
1903. It is important to correct any problems with personal
names and other information so that researchers will be able
to search by keyword and be assured of retrieving desired
results. ... type fonts cause a great deal of difficulty in
digitizing the text and can cause problems for searchers. Also,
many of the guests' names at Tent City and Hotel Del
Coronado were taken from the registration books and reported
in the Program. This led to many problems in spelling of last
names and the editors were not careful to be consistent in the
spellings. This Program is an important resource since it
provides an excellent picture of daily life in Tent City and
captures much of the history of Coronado itself.”
Gene, California
Personal communications with CDNC text correctors.
Motivation
CDNC users’ report
54. “I have always been interested in history, especially the
development of the American West, and nothing brings it alive
better than newspapers of the time. I believe them to be an
invaluable source of knowledge for us and future generations.”
David, United Kingdom
Personal communications with CDNC text correctors.
Motivation
CDNC users’ report
55. CDNC is an excellent source of information matching my
personal interest in such topics as sea history, development
of shipbuilding, clippers and other ships etc. ...
Unfortunately, the quality of text ... is rather poor I’m
afraid. This is why I started to do all corrections necessary
for myself ... and to leave the corrected text for use of
others. .... I am not doing this very regularly as this is just
my hobby and pleasure.
Jerzey, Poland
Personal communications with CDNC text correctors.
Motivation
CDNC users’ report
56. As an amateur historical researcher my time for research is very
limited. Making time to travel to archives, libraries, and historical
societies does not happen as often as I would like. The Cambridge
Public Library’s online newspaper collection has been an invaluable
resource and it is fun. I am very grateful for all the help I have received
over the years from so many research organizations. Correcting text
has several benefits. It makes it much more likely that I will find a
story if I decide to search for it in the future. It is a way of saying
‘thank you’ to the Cambridge Library for having such a great resource
available and maybe I can make the next person’s research a little
easier. It is my own little historical preservation project.
Daniel, Massachusetts
Personal communications with Cambridge text correctors.
Motivation
Cambridge users’ report
57. Graphic from Kaufmann et al. “More than fun and money. Worker Motivation
in Crowdsourcing – A Study on Mechanical Turk.”
Motivation
59. the long tail* of crowdsourced
OCR text correction
a probability distribution has a long tail if a larger share
of the population rests within its tail than would be the
case if the population were normally distributed.
the most productive users represent a small fraction of
the total user population and complete ~50% of total
production.
said a different way, less productive users represent the
largest fraction of the user population and also complete
~50% of the total production.
The phrase “long tail” was popularized by Chris Anderson in the October 2004 Wired magazine article The Long Tail
and by Clay Shirky’s February 2003 essay “Power laws, web logs, and inequality”.
60. OCR text correction long tails
0
75000
150000
225000
300000
CDNC lines corrected by text corrector
0
750,000
1,500,000
2,250,000
3,000,000
NLA lines corrected by text corrector
top corrector 242,965 top corrector 1,456,906
50%
50%
50%
50%
Statistics from Oct 2012
62. Website traffic
After a crowdsourcing transcription project of diaries from the
American War Between the States, Nicole Saylor, Head of Digital
Library Services at the University of Iowa Libraries, reported
“On June 9, 2011, we went from about 1000
daily hits to our digital library on a really good
day to more than 70,000.”
Nicole Saylor interviewed by Trevor Owens. “Crowdsourcing the Civil War: Insights Interview with Nicole Saylor” blog post
at http://blogs.loc.gov/digitalpreservation/2011/12/crowdsourcing-the-civil-war-insights-interview-with-nicole-saylor/.
Dec 6, 2011.
63. Website traffic
Website traffic at CDNC before / after implementing
crowdsourcing
before crowdsourcing
11-Jun-2011 / 12-Jul-2011
after crowdsourcing
11-Jun-2012 / 12-Jul-2012
change
visits 17,485 21,488 +22.9%
unique visitors 11,381 13,376 +17.5%
visit duration 9m 24s 11m 7s +18.3%
bounce rate 51.3% 44.5% -6.8%
pages per visit 14.9 11.7 -21.5%
65. Accuracy
“His Accuracy Depends on Ours!"
Office for Emergency Management. Office of War
Information. Domestic Operations Branch. Bureau of
Special Services. [Photo held at US National Archives and
Records Administration]
67. Deaths. lln»rieff, Esq. of <c .. Qn.
Sunday, the till. greatly Drandrellt, of
Orms4irJi.- ~ ; ;✓ ' • * On ijfr r inn
l j j j i l F i i j ' 1 1 f H a v o d i v y d ,
Carnarvonshire, S ; **" *- ' « ' March
Oxford, F. Tfovmeud, Uerald. » • V .
•On Tncsdav last, Mr. Charles.
IWilinson, this 8 ; had vf thesis#,, a
week ago, which tcrminate<i'iu his
death. . / ' ■ O'i Sunday, dJst nit. at.
AsbtCnvHall, mar Lancaster,
Mr.,Geo. Worn ick, many years
house'steward hit late Once The
Hamilton and Brandon. He locked
himself h»oWn'r«wte<: soon. twelve
o'clock" that dny, and fii»-d a loaded
pistol "through Ins bead, 1 which
instantaneously killed him. Coronet's
Verdict, shot himself in a temporary fit of
Friday week,
raw OCR text
Excerpt from The British Newspaper Archive, Chester Courant, Tuesday 6-Apr-1819, page 3.
newspaper image
68. Accuracy
• Edwin Kiljin (Koninklijke Bibliotheek the Netherlands)
reports raw OCR character accuracies of 68% for early 20th
century newspapers
• Rose Holley (National Library of Australia) reports raw
OCR character accuracy varied from 71% to 98% on a
sample Trove digitized newspapers
Rose Holley. “How good can it get? Analysing and improving OCR accuracy in large scale historic newspaper digitisation
programs. D-Lib Magazine. March/April 2009.
Edwin Kiljin. “The current state-of-art in newspaper digitization.” D-Lib Magazine. January/February 2008.
Public domain graphic courtesy of Wikimedia Commons.
69. Accuracy
MAPPING TEXTS* assesses digitization quality of
digital newspapers by comparing the number of words
recognized to the total number of words scanned
* Mapping texts is a collaboration between the University of North Texas and Stanford University aimed at experimenting
with new methods for finding and analyzing meaningful patterns embedded in massive collections of digital newspapers.
70. uncorrected OCR accuracy by
newspaper title
Title
OCR character
accuracy
~OCR word
accuracy*
PRP Pacific Rural Press 1871 - 1922 92.6% 68.1%
SFC San Francisco Call 1890 - 1913 92.6% 68.1%
LAH Los Angeles Herald 1873 - 1910 88.7% 54.9%
LH Livermore Herald 1877 - 1899 88.6% 54.6%
DAC Daily Alta California 1841 - 1891 88.2% 53.4%
CFJ California Farmer and Journal
of Useful Sciences 1855 - 1880
86.5% 48.4%
SN Sausalito News 1885 - 1922 70.4% 17.3%
*Word accuracy assumes average word length is 5 characters
71. OCR accuracy by newspaper title
Title
OCR character
accuracy
Corrected
accuracy
PRP Pacific Rural Press 1871 - 1922 92.6% 99.3%
SFC San Francisco Call 1890 - 1913 92.6% 99.6%
LAH Los Angeles Herald 1873 - 1910 88.7% 99.1%
LH Livermore Herald 1877 - 1899 88.6% 99.9%
DAC Daily Alta California 1841 - 1891 88.2% 99.9%
CFJ California Farmer and Journal
of Useful Sciences 1855 - 1880
86.5% 99.8%
SN Sausalito News 1885 - 1922 70.4% 100.0%
72. corrected accuracy
by newspaper title
Title
OCR character
accuracy
~OCR word
accuracy*
Corrected
accuracy
~Corrected
word accuracy*
PRP 1871 - 1922 92.6% 68.1% 99.3% 96.5%
SFC 1890 - 1913 92.6% 68.1% 99.6% 98.0%
LAH 1873 - 1910 88.7% 54.9% 99.1% 95.6%
LH 1877 - 1899 88.6% 54.6% 99.9% 99.5%
DAC 1841 - 1891 88.2% 53.4% 99.9% 99.5%
CF 1855 - 1880 86.5% 48.4% 98.3% 91.8%
SN 1885 - 1922 70.4% 17.3% 100.0% 100.0%
*Word accuracy assumes average word length is 5 characters
73. correction accuracy by user
User
Average OCR
accuracy
Correction
accuracy
A 70.4% 100.0%
B 87.1% 99.5%
C 95.4% 99.5%
D 86.5% 98.3%
E 95.3% 100.0%
F 91.0% 100.0%
G 91.0% 99.8%
H 90.5% 99.0%
I 96.6% 99.8%
J 94.8% 100.0%
K 86.8% 99.3%
74. How does low text accuracy affect search recall?
The Facts
• Average uncorrected OCR character accuracy of the
CDNC sample data is ~89%
• Average length of an English word is 5 characters
• Average word accuracy is 89% x 89% x 89% x 89% x 89%
= 55.8% - round up to 60% or 6 out of 10 words correct
Accuracy
76. Accuracy
The Facts
• Average corrected character accuracy of the CDNC
sample data is ~99.4%
• Average word accuracy of CDNC corrected text is 99.4%
x 99.4% x 99.4% x 99.4% x 99.4% = 97.0%
78. A search for “Arndt” at Chronicling America gives
10,267 results*
• If Chronicling America text accuracy is 55.8% (same as
uncorrected CDNC sample), then 8,133 instances of
“Arndt” were not found
• If text accuracy is 97.0%, then 317 instances of “Arndt”
were not found
Accuracy
* Search performed 31 Oct 2012
79. Accuracy
Suppose the word/name is longer than 5
characters?
The Facts
• Assume that average uncorrected / corrected OCR
character accuracy is ~89% / ~99% same as CDNC.
Name Name length Raw text accuracy Corrected text accuracy
Eklund 6 49.7% 94.2%
Kennedy 7 44.2% 93.25
Espinosa 8 39.4% 92.3%
Bonaparte 9 35.0% 91.4%
Chatterjee 10 31.2% 90.4%
80. Accuracy
Name
Number of
search results
Missing results with
raw text accuracy
Missing results with
corrected text accuracy
Eklund 2,951 2,987 182
Kennedy 360,723 455,392 26,111
Espinosa 1,918 2,950 160
Bonaparte 44,664 82,947 4,203
Chatterjee 19 42 2
Chronicling America searches done 19-Mar-2013
(6,025,474 pages from 1836 to 1922).
82. $
Economic benefits
Financial value of outsourced OCR text correction
for newspapers?
The Assumptions
• 25 to 50 characters per line in a newspaper column:
Assume 40 characters per line (CDNC sample average)
• Outsourced text transcription or correction costs USD
$0.35 to $1.20 per 1000 characters: Assume $0.50
per 1000 characters
83. $
Economics
$ 578,000 lines x 40 characters per line x
1/1000 x $0.50 = $11,560
$ 68,908,757 lines x 40 characters per line x
1/1000 x $0.50 = $1,378,175
84. $
Economics
Financial value of in-house OCR text
correction?
The Assumptions
• Correction takes 15 seconds per line
• Cost is hourly wage plus benefits of lowest level
employee, $10 for CDNC, $41.88* for Australia
AUD $40.38 = USD $41.88 is the actual labor value assumed by the National Library of Australia to calculate avoided costs
due to crowdsourced OCR text correction in its 2012 Trove Status Report.
85. $
Economics
$ 578,000 lines x 15 seconds per line x 1/3600 hrs
per second x $10.00 per hr = $24,083
$ 68,908,757 lines x 15 seconds per line x 1/3600
hrs per second x $41.88 per hr = $12,024,578
87. “when someone transcribes a document, they are
actually better fulfilling the mission of a cultural
heritage organization than someone who simply stops
by to flip through the pages”
Other benefit
Paraphrased from Trevor Owen’s blog http://www.trevorowens.org/2012/03/
crowdsourcing-cultural-heritage-the-objectives-are-upside-down/ (accessed June 2013).
88. “in addition to increasing search accuracy or lowering
the costs of document transcription, crowdsourcing is
the single greatest advancement in getting people using
and interacting with library collections”
Other benefit
Paraphrased from Trevor Owen’s blog http://www.trevorowens.org/2012/03/
crowdsourcing-cultural-heritage-the-objectives-are-upside-down/ (accessed June 2013).
89. Crowdsourcing considerations
• How to market / advertise
crowdsourcing?
• How to motivate
crowdsourcers?
• Is authentication / identity of
crowdsourcers an issue?
• How to administer
crowdsourced data?
Photo of Aleister Crowley [Public domain] from Wikimedia
Commons
90. Conclusions
Conclusion of the Sonata for piano #32, opus 111 by
Ludwig van Beethoven
• Lots of crowdsourcing in cultural heritage
organizations and elsewhere
• Benefits are multi-faceted: Economic, data
accuracy, patron engagement, increased web
traffic
91. Resources
Public domain photo “A useful instruction for young sailors from the Royal
Hospital School, Greenwich” from the National Maritime Museum.
92. Comprehensive worldwide list of online
newspaper archives
Wikipedia contributors, "List of online newspaper archives," Wikipedia, The Free Encyclopedia, https://
en.wikipedia.org/wiki/Wikipedia:List_of_online_newspaper_archives (accessed March 17, 2013).
93. Search many digital newspaper
collections at once!
As of June 2013 elephind (http://www.elephind.com) has indexed 1,032 newspapers from 13
historical digital collections comprising 1,097,928 issues and 45,243,443 pages/articles.
94. Correct California newspapers at http://cdnc.ucr.edu
Correct Cambridge MA newspapers http://bit.ly/cambridgepublic
Correct Australian newspapers http://trove.nla.gov.au
Correct Tennessee newspapers http://tndp.lib.utk.edu
Correct Virginia newspapers http://virginiachronicle.com
Try crowdsourcing!
95. Hãy thử crowdsourcing!
Or try Russian language periodicals http://bit.ly/russianperiodicals
Correct Vietnamese newspapers http://bit.ly/nationallibraryofvietnam
Попробуйте краудсорсинга!
96. Other resources
Mapping Texts at http://mappingtexts.stanford.edu/
Wragge Labs at http://wraggelabs.com/
Wikipedia list of crowdsourcing projects
https://en.wikipedia.org/wiki/
List_of_crowdsourcing_projects