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The Hitchhiker's Guide to the Future of Digital Humanities

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Slides of my DHOxSS closing lecture
Oxford, 26 July 2019

Abstract
In the constellation of research fields, new configurations are continuously reshaping our ideas of what a field should be. This is particularly the case in the young field of digital humanities which, as David M. Berry noted, started with a focus on improving access to digital repositories and then moved to expanding the limits of archives to include born-digital materials as research objects. Both moves greatly impacted our research practice. However, I argue that we have only started scratching the surface of what digital methods can mean for humanities research.



In particular, as our methods and collaborations with other fields have matured, we can now start imagining new types of research questions that go beyond the sum of their ‘digital’ and ‘humanities’ parts -- to fundamentally change the nature of the humanities questions that we can ask. For such a reshaping to occur, we need to deepen the connection to our academic neighbours and keep looking beyond our own research community in order to ask these new questions. In my talk, I will present how multi-disciplinary collaborations between historians, linguists, and computer scientists can bring about new insights that may form the first steps to this future.

Veröffentlicht in: Technologie
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The Hitchhiker's Guide to the Future of Digital Humanities

  1. 1. D I G I TA L H U M A N I T I E S L A B The Hitchhiker's Guide to the Future of Digital Humanities Marieke.van.Erp@dh.huc.knaw.nl merpeltje D I G I TA L H U M A N I T I E S L A B
  2. 2. D I G I TA L H U M A N I T I E S L A B
  3. 3. D I G I TA L H U M A N I T I E S L A B Current developments • Digital Humanities is covering more and more topics: • DH2019: language, literature, history, geohumanities, scholarly communication, infrastructure, society, media, politics, cultural heritage, art, food, image analysis, pedagogy, culture • Technology is getting better: • machine learning, OCR, HTR, natural language processing, computer vision, data science • How far can we go?
  4. 4. Image source: https://twelvemilesfromalemondotcom.files.wordpress.com/2014/09/img_0326.jpg
  5. 5. D I G I TA L H U M A N I T I E S L A B Sweet connections Ulbe Bosma Economic & Social History
  6. 6. D I G I TA L H U M A N I T I E S L A B Sweet connections Rebeca Ibañez-Martín Anthropologist Food, body & wellbeing Ulbe Bosma Economic & Social History
  7. 7. D I G I TA L H U M A N I T I E S L A B Sweet connections Rebeca Ibañez-Martín Anthropologist Food, body & wellbeing Ulbe Bosma Economic & Social History Information extraction & modelling
  8. 8. D I G I TA L H U M A N I T I E S L A B Sweet connections Rebeca Ibañez-Martín Anthropologist Food, body & wellbeing Ulbe Bosma Economic & Social History Sugar Information extraction & modelling
  9. 9. D I G I TA L H U M A N I T I E S L A B Sweet connections Rebeca Ibañez-Martín Anthropologist Food, body & wellbeing Ulbe Bosma Economic & Social History Proxy for historical sugar consumption: apple pie recipes Information extraction & modelling
  10. 10. D I G I TA L H U M A N I T I E S L A B Global apple pie Food, body, wellbeing: do recipes reflect cultural ideas about sugar? Economic & Social History: do recipes follow sugar import/ export trends? Linguistics & semantic web: how to automatically extract and model the evolution of apple pie recipes
  11. 11. D I G I TA L H U M A N I T I E S L A B Pop quiz: apple pie ingredients
  12. 12. D I G I TA L H U M A N I T I E S L A B Source: The Evening Star, Washington D. C. 22 September 1949, page B-4 https://chroniclingamerica.loc.gov/lccn/sn83045462/1949-09-22/ed-1/seq-37.pdf
  13. 13. D I G I TA L H U M A N I T I E S L A B Knowledge Graphs and Artificial Intelligence 30 June - 6 July | Bertinoro | Italy http://stlab.istc.cnr.it/isws/wordpress/ European Languages and Literature Digital Humanities Cultural Heritage Automating Medical Reporting Semantic Interoperability Open Scholarly Citations
  14. 14. Team Delorean, “Pimp my Pie”, 2019-07-06, ISWS 2019 1. Searched for newspaper pages with recipes in: a. National Library of the Netherlands b. Library of Congress’ Chronicling America Corpus 2. Downloaded OCR text of the pages 3. Analysed these texts to filter false positives a. Dutch: 62 prefilter, 18 after filter b. American: 328 prefilter, ~100 after filter 4. Transformed remaining recipes text to structured data Getting useful data
  15. 15. Team Delorean, “Pimp my Pie”, 2019-07-06, ISWS 2019
  16. 16. D I G I TA L H U M A N I T I E S L A B Recipe comparison
  17. 17. From newspapers to a recipe web D I G I TA L H U M A N I T I E S L A B Ingredients Recipe tags Recipe descriptions Recipe articles Information Extraction and Multilabel Classification Enrichment Ingredient and quantity extraction Recipe tags Structured newspaper recipes Origin DBpedia link Scientific name Recipe text detection Structured and enriched newspaper recipes Seed list Text classification Marieke van Erp, Melvin Wevers and Hugo Huurdeman (2018) Constructing a Recipe Web from Historical Newspapers. Proceedings of ISWC 2018. Monterey, California, USA, 8-12 October 2018.
  18. 18. Dealing with noisy text • 100 articles were manually annotated using Recogito • OCR errors in ingredients or quantities marked separately • IAA .85 but OCR boundaries difficult: jºar,anen’ vs ◦ºar,anen’ D I G I TA L H U M A N I T I E S L A B Marieke van Erp, Melvin Wevers and Hugo Huurdeman (2018) Constructing a Recipe Web from Historical Newspapers. Proceedings of ISWC 2018. Monterey, California, USA, 8-12 October 2018.
  19. 19. Next steps in understanding apple pie • Expand corpus in time and geographical spread (Germany, Austria, Argentina) • Analyse quantities (incl. old measures) & connect to import/export • Expand concept drift evolution • Connect to Meertens Institute surveys on food habits Image source: https://cdn.pixabay.com/photo/2014/11/15/20/30/kitchen-scale-532651_960_720.jpg D I G I TA L H U M A N I T I E S L A B
  20. 20. Image credit: Menno den Engelse
  21. 21. D I G I TA L H U M A N I T I E S L A B CLARIAH ATM TEAM Ivo Zandhuis & Richard Zijdeman Socio-economic history
  22. 22. D I G I TA L H U M A N I T I E S L A B CLARIAH ATM TEAM Ivo Zandhuis & Richard Zijdeman Socio-economic history Julia Noordegraaf, Thunnis van Oort Vincent Baptist & Claartje Rasterhoff Entertainment Culture
  23. 23. D I G I TA L H U M A N I T I E S L A B CLARIAH ATM TEAM Ivo Zandhuis & Richard Zijdeman Socio-economic history Mark Raat, Thomas Vermaut & Hans Mol Historical Geographical Information Systems Julia Noordegraaf, Thunnis van Oort Vincent Baptist & Claartje Rasterhoff Entertainment Culture
  24. 24. D I G I TA L H U M A N I T I E S L A B CLARIAH ATM TEAM Ivo Zandhuis & Richard Zijdeman Socio-economic history Nicoline van der Sijs, Kristel Doreleijers, Marieke van Erp Language variation Mark Raat, Thomas Vermaut & Hans Mol Historical Geographical Information Systems Julia Noordegraaf, Thunnis van Oort Vincent Baptist & Claartje Rasterhoff Entertainment Culture
  25. 25. D I G I TA L H U M A N I T I E S L A B Socio-economic History: The Amsterdam Elite Slide credit: Ivo Zandhuis
  26. 26. D I G I TA L H U M A N I T I E S L A B Electorate and Elite (dr B. de Vries, 1986) ● Reuse of “old” SPSS-data ● Electoral rolls 1854 & 1884 ● Data contains: ○ Address ○ Age ○ Occupation ○ ... Slide credit: Ivo Zandhuis
  27. 27. D I G I TA L H U M A N I T I E S L A B Compare elite data ● using census-data (CEDAR) ● using HISCLASS ● using pictures from the City Archives Amsterdam Elite-density 1854 (with QGIS) Slide credit: Ivo Zandhuis
  28. 28. D I G I TA L H U M A N I T I E S L A B Linguistics: 19 Neighbourhoods, 19 dialects?
  29. 29. D I G I TA L H U M A N I T I E S L A B Resources ● Primary and secondary sources about Amsterdam dialect(s) in the 19th century (e.g. dictionaries, glossaries, historical descriptions of the city and/or specific neighbourhoods) ● Recordings of dialect speakers born in the late 19th or early 20th century (Nederlandse Dialectenbank, Nederlandse Liederenbank) ● Results of a survey on the pronunciation and words of the Amsterdam dialect, conducted in 1877
  30. 30. D I G I TA L H U M A N I T I E S L A B Data collection ● We selected 8,020 relevant data points from all sources (relevant data point = dialect or sociolect words or features that are indicated as prominent, salient for nineteenth-century Amsterdam as a whole, (a) specific neighbourhood(s) or social class ● We subsequently store and organise these data points in a large database (first in Excel/FileMaker, then RDF)
  31. 31. D I G I TA L H U M A N I T I E S L A B Entertainment (culture) matters! ● Existing Dutch databases ○ Cinema Context ○ ONSTAGE ● Urban entertainment and nightlife ○ Need for further research ○ Going beyond dichotomy of high/low culture Slide credit: Vincent Baptist
  32. 32. D I G I TA L H U M A N I T I E S L A B Integrated geospatial research ● Extending historical data on nightlife ○ Registers of night licenses ○ Address books ● Shared research infrastructure and location points ○ Going beyond dichotomy of high/ low culture Slide credit: Vincent Baptist
  33. 33. D I G I TA L H U M A N I T I E S L A B 1870 1891 1911 Neighborhood A ↔ Neighborhood YY Slide credit: Vincent Baptist
  34. 34. What’s an address? ● administrative concept
 ● stand-alone concept
 ● cross-domain Slide credit: Thomas Vermaut
  35. 35. Slide credit: Thomas Vermaut
  36. 36. Amsterdam Time Machine ● 52,684 proxies for location
 ● semantic concept + geographical location
 ● knowledge graph Slide credit: Thomas Vermaut
  37. 37. D I G I TA L H U M A N I T I E S L A B Data connections through time and space Image via: Julia Noordegraaf
  38. 38. D I G I TA L H U M A N I T I E S L A B What the time machine enables • Investigate sociolects in conjunction with social mobility (linguistic features + extralinguistic features) • Investigate distribution of the elite, elite density per neighbourhood and relation between status and elite density • Investigate correlations between cinema locations, socio-demographic profiles of their neighbourhoods through the link with house prices, population development and density.
  39. 39. D I G I TA L H U M A N I T I E S L A B Average yearly house rent (1919) per Neighbourhood (1909) Based on slide by: Vincent Baptist, Julia Noordegraaf & Thunnis van Oort
  40. 40. D I G I TA L H U M A N I T I E S L A B Where to start?
  41. 41. D I G I TA L H U M A N I T I E S L A B Get to know your colleagues
  42. 42. D I G I TA L H U M A N I T I E S L A B Where can you start? • Learn to speak each other’s language • Give it time • Start small • Stay curious • Tinker • Check out seminars at different faculties / Science & Cocktails / Pint of Science / Hackathon events • Skip your colleagues’ presentations at conferences: go see something new • Start today!

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