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20130412 Productivity of the crowd [acrl indianapolis]
 

20130412 Productivity of the crowd [acrl indianapolis]

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crowdsourcing at digital historical newspapers collections at libraries around the world.

crowdsourcing at digital historical newspapers collections at libraries around the world.

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    20130412 Productivity of the crowd [acrl indianapolis] 20130412 Productivity of the crowd [acrl indianapolis] Presentation Transcript

    • Productivity of the crowd Slides @ http://bit.ly/crowdsourceacrl2013 Frederick Zarndt Chair, IFLA Newspapers Section CCS / Digital Divide Data / DL Consulting @cowboyMontana, #crowdsourceacrl2013 frederick@frederickzarndt.com Brian Geiger Director, Center for Bibliographic Studies and Research bgeiger@ucr.eduPhoto held by John Oxley Library, State Library ofQueensland. Original from Courier-mail, Brisbane,Queensland, Australia.
    • News
    • Crowds
    • Crowds + News
    • Demographics“British volunteers for "Kitcheners Army" waiting for their pay in thechurchyard of St. Martin-in-the-Fields, Trafalgar Square, London”Public domain photo from Imperial War Museum
    • purpose / motive /reason 50%
    • purpose / motive /reason 50%
    • purpose / motive /reason 72%
    • purpose / motive /reason 80%
    • purpose / motive /reason 67%
    • ?Photo held by John Oxley Library, State Library ofQueensland. Original from Courier-mail, Brisbane,Queensland, Australia.
    • age50%
    • age80%
    • age67%
    • ?Photo held by John Oxley Library, State Library ofQueensland. Original from Courier-mail, Brisbane,Queensland, Australia.
    • User Demographic genealogists and family historians 50+ years old • In 2012 the National Library of Australia reported that ~50% of Trove users are family historians PAPERSPAST • National Library of New Zealand survey found that ~50% of PapersPast users are genealogists • A 2013 California Digital Newspaper Collection survey shows that more than 65% of its users are genealogists; 75% are 50 years old or older • A 2012 Utah Digital Newspapers survey showed that 72% of its users are genealogists* • A 2013 Cambridge Public Library survey shows that more than 80% of its users are genealogists; 73% are 50 years old or older*John Herbert and Randy Olsen. “Small town papers: Still delivering the news”. Paper given at 2012 WorldLibrary and Information Congress. Helsinki. August 2012.
    • raw OCR text newspaper imageDeaths. lln»rieff, Esq. of <c .. Qn.Sunday, the till. greatly Drandrellt, ofOrms4irJi.- ~ ; ;✓ • * On ijfr r innljjjil F iij 11 f Havodivyd,Carnarvonshire, S ; **" *- « MarchOxford, F. Tfovmeud, Uerald. » • V .•On Tncsdav last, Mr. Charles.IWilinson, this 8 ; had vf thesis#,, aweek ago, which tcrminate<iiu hisdeath. . / ■ Oi Sunday, dJst nit. at.AsbtCnvHall, mar Lancaster,Mr.,Geo. Worn ick, many yearshousesteward hit late Once TheHamilton and Brandon. He lockedhimself h»oWnr«wte<: soon. twelveoclock" that dny, and fii»-d a loadedpistol "through Ins bead, 1 whichinstantaneously killed him. CoronetsVerdict, shot himself in a temporary fit ofFriday week,Excerpt from The British Newspaper Archive, Chester Courant, Tuesday 6-Apr-1819, page 3.
    • 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 newspapersEdwin Kiljin. “The current state-of-art in newspaper digitization.” D-Lib Magazine. January/February 2008.Rose Holley. “How good can it get? Analysing and improving OCR accuracy in large scale historic newspaperdigitisation programs. D-Lib Magazine. March/April 2009.Public domain graphic images courtesy of Wikimedia Commons.Graphic is logo for Accuracy in Media (http://www.aim.org/)
    • 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)
    • MotivationGraphic from Kaufmann et al. “More than fun and money. Worker Motivation inCrowdsourcing – A Study on Mechanical Turk.”
    • You can make a differenceGraphic courtesy of TYPEinspire (http://typeinspire.com/)
    • User Lines corrected Lines corrected User 1 242,965 1,456,906 1 2 87,515 1,385,369 2 3 31,318 1,010,360 3 4 24,144 960,230 4 5 23,184 847,340 5 6 19,240 786,147 6 7 18,898 657,187 7 8 16,875 600,513 8 9 11,784 582,276 9 10 9,762 565,384 10Statistics from Oct 2012
    • uncorrected OCR accuracy by newspaper title raw character ~raw word Title accuracy 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 86.5% 48.4% of Useful Sciences 1855 - 1880 SN Sausalito News 1885 - 1922 70.4% 17.3%*Word accuracy assumes average word length is 5 characters
    • corrected OCR accuracy by newspaper title raw character corrected Title accuracy accuracyPRP 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 86.5% 99.8%of Useful Sciences 1855 - 1880SN Sausalito News 1885 - 1922 70.4% 100.0%
    • corrected OCR accuracy by newspaper title raw character ~raw word corrected ~corrected word Title accuracy accuracy* accuracy 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
    • correction accuracy by user average uncorrected average corrected User text accuracy text 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%
    • Crowdsourcing benefits Public domain photo courtesy of US Navy
    • $ Economics 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
    • $ 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
    • $ 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 AustraliaAUD $40.38 = USD $41.88 is the actual labor value assumed by the National Library of Australia to calculateavoided costs due to crowdsourced OCR text correction in its 2012 Trove Status Report.
    • $ 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
    • Accuracy“His Accuracy Depends on Ours!"Office for Emergency Management. Office of War Information.Domestic Operations Branch. Bureau of Special Services. [Photoheld at US National Archives and Records Administration]
    • Accuracy How does low text accuracy affect search recall? The Facts Average uncorrected OCR character accuracy of the CDNC 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 correctPublic domain graphic images courtesy of Wikimedia Commons.
    • Search recall no text correction T ARND D R N A T ARNDT ARNDT ARNDT instances of “ARNDT” found instances of “ARNDT” not found ARNDT ARNDT ARNDT ARNDT ARNDTPublic domain graphic images courtesy of Wikimedia Commons.Image © Nevit Dilmen found at Wikimedia commons
    • Accuracy The Facts Average corrected character accuracy of the CDNC data is ~99.4% Average word accuracy of the CDNC corrected text is 99.4% x 99.4% x 99.4% x 99.4% x 99.4% = 97.0%Public domain graphic images courtesy of Wikimedia Commons.
    • Search recall with text correction ARNDT ARNDT ARNDT instances of “ARNDT” found ARNDT ARNDT instances of “ARNDT” not found ARNDT ARNDT ARNDT ARNDT AR ND TPublic domain graphic image courtesy of Wikimedia Commons.Image © Nevit Dilmen found at Wikimedia commons
    • Accuracy A search for my grandmother’s maiden name “Arndt” gives 11,154 results** Search performed 8 April 2013Public domain graphic image courtesy of Wikimedia Commons.
    • Accuracy A search for my grandmother’s maiden name “Arndt” gives 11,154 results* If text accuracy is 55.8% (same as uncorrected CDNC sample), then 8,835 instances of “Arndt” were not found* Search performed 8 April 2013Public domain graphic images courtesy of Wikimedia Commons.
    • Accuracy A search for my grandmother’s maiden name “Arndt” gives 11,154 results* If text accuracy is 55.8% (same as uncorrected CDNC sample), then 8,835 instances of “Arndt” were not found If text accuracy is 97.0%, then 345 instances of “Arndt” were not found* Search performed 8 April 2013Public domain graphic images courtesy of Wikimedia Commons.
    • Accuracy Suppose the 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%Public domain graphic images courtesy of Wikimedia Commons.
    • Accuracy Searches done 19-Mar-2013 (6,025,474 pages from 1836 to 1922). Number of Missing results with Missing results with Name search results raw text accuracy 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 2Public domain graphic images courtesy of Wikimedia Commons.
    • Hard-to-measure-but-shouldn’t-be-overlooked benefits Public domain photo “A useful instruction for young sailors from the Royal Hospital School, Greenwich” from the National Maritime Museum.
    • HTMBSBO benefit “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”Paraphrased from Trevor Owen’s Crowdstorming blog http://crowdstorming.wordpress.com/
    • HTMBSBO benefit “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”Paraphrased from Trevor Owen’s Crowdstorming blog http://crowdstorming.wordpress.com/
    • 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.
    • Conclusion of the Sonata for piano #32, opus 111 by Ludwig van Beethoven
    • ? Slides @ http://bit.ly/crowdsourceacrl2013 Frederick Zarndt Chair, IFLA Newspapers Section CCS / Digital Divide Data / DL Consulting @cowboyMontana, #crowdsourceacrl2013 frederick@frederickzarndt.com Brian Geiger Director, Center for Bibliographic Studies and Research bgeiger@ucr.eduPhoto held by John Oxley Library, State Library ofQueensland. Original from Courier-mail, Brisbane,Queensland, Australia.
    • Try crowdsourcing! Correct California newspapers at http://cdnc.ucr.edu Correct Australian newspapers http://trove.nla.gov.au Correct Cambridge MA newspapers http://bit.ly/cambridgepublic Correct Tennessee newspapers http://tndp.lib.utk.edu Correct Virginia newspapers http://virginiachronicle.com
    • Hãy thử crowdsourcing! Correct Vietnamese newspapers http://bit.ly/nationallibraryofvietnamПопробуйте краудсорсинга! Or try Russian language periodicals http://bit.ly/russianperiodicals Kokeile crowdsourcing! Or try Finnish newspapers http://digi.lib.helsinki.fi/sanomalehti
    • MotivationGraphic from Kaufmann et al. “More than fun and money. Worker Motivation inCrowdsourcing – A Study on Mechanical Turk.”
    • Motivation Trove users’ report • “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 CDNC users’ report “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, CaliforniaPersonal communications with CDNC text correctors.
    • Motivation CDNC users’ report “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, CaliforniaPersonal communications with CDNC text correctors.
    • Motivation CDNC users’ report “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, CaliforniaPersonal communications with CDNC text correctors.
    • Motivation CDNC users’ report “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 KingdomPersonal communications with CDNC text correctors.
    • Motivation CDNC users’ report 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, PolandPersonal communications with CDNC text correctors.
    • Other resourcesMapping 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
    • As of 17-Mar-2013 the National Library of Australia’s (http://trove.nla.gov.au/) Alexa Internet traffic rank is 14,490 (global) / 330 (Australia). Trove gets ~75% of all National Library web traffic.
    • National Library of Australia • Online since 2008 • 8,000,000+ pages • Top text corrector 1,772,090 lines • 2,400,000+ lines corrected each month (average for Mar 2012 to Mar 2013) • 90,489,875 lines corrected as of Mar 2013, up from 61,682,883 lines corrected Mar 2012 • 88,935 total registered users • 8,743 active usersStatistics from private communication with the National Library of Australia Oct 2012
    • As of 17-Mar-2013 National Library of Finland’s (http://www.nationallibrary.fi/) Alexa Internet global traffic rank is 4,303,901. Its Internet traffic rank for Finland was 199 as of 2-Apr-2012.
    • 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
    • As of 17-Mar-2013 UC Riverside’s Alexa Internet traffic rank is 11,782 (global) / 4,120 (USA). CDNC gets ~3.30% of all UC Riverside web traffic.
    • California Digital Newspaper Collection• CDNC began digitizing newspapers in 2005 as part of the Library of Congress National Digital Newspapers Program (NDNP)• Newspapers digitized to article-level in addition to page-level as required by NDNP (same as Utah Digital Newspapers)• Since 2009 hosted on Veridian at http://cdnc.ucr.edu• Collection size 55,970 issues, 495,175 pages, 5,658,224 articles, 498,000,000+ lines (Mar 2013)
    • OCR text correction• OCR text correction added August 2011• Corrections are done line by line• ~578,000+ lines of text corrected Oct 2012• ~935,398+ lines of text corrected Mar 2013• ~2% of the collection corrected, 98% to go!• Top corrector 327,244 lines > 2x 2nd corrector
    • 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 (Mar-2013)• Collection includes 13,099 obituary cards