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Data Science in the Humanities

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Slides presented at the 10th DataScienceNL Meetup, 20 June 2019 at PICNIC in Amsterdam.

Abstract:
Humanities is a broad research field that covers (but is not limited to) history, literature studies, linguistics and ethnology. Digital methods are becoming more common in day-to-day humanities practice, but these methods don’t always translate seamlessly to the humanities domain. In this talk, I will present use cases from the Dutch Royal Academy’s Humanities Cluster that show the potential as well as frictions in the application of digital analysis methods in this domain.

https://www.meetup.com/en-AU/DataScienceNL/events/260563513/

Published in: Technology
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Data Science in the Humanities

  1. 1. Data Science in the 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 ©Archief.AmsterdamKLAG06095000041
  2. 2. This talk • About DHLab • Places • Concepts and words • Crossing genres • Work in progress • Discussion D I G I TA L H U M A N I T I E S L A B
  3. 3. Digital Humanities Lab • Started in September 2017 • Our goal is to advance humanities research through digital methods & • Make digital methods more ‘culturally aware’ • Focus on big ‘textual’ data (mostly) • Interdisciplinary • Inter-institutional (joint research group of Huygens ING, IISH and Meertens Institute) Melvin Wevers Adina Nerghes Marieke van Erp D I G I TA L H U M A N I T I E S L A B
  4. 4. Places Image credit: Menno den Engelse
  5. 5. Amsterdam locations Slide credit: Ivo Zandhuis
  6. 6. Image source: https://beeldbank.amsterdam.nl/afbeelding/D10098000035 1866 Cholera per neighbourhood map Amsterdam City Archive
  7. 7. Amsterdam Neighbourhoods 1850 Image source: http://www.amsterdamhistorie.nl/buurten/buurten1850.html
  8. 8. Linguistics: 19 Neighbourhoods, 19 dialects?
  9. 9. Data connections through time and space Image via: Julia Noordegraaf
  10. 10. Issues connecting locations through time • Naming conventions • Dialects: neighbourhoods defined by researchers • Electorate: 1850 neighbourhood indicators • Other sources (e.g. cinema locations): street names (as we know them now) • Street names + numbering have changed over time D I G I TA L H U M A N I T I E S L A B Image source:https://upload.wikimedia.org/wikipedia/commons/0/0a/ Kaart_van_Amsterdam_in_vogelvlucht%2C_anno_1544.jpeg
  11. 11. Core map Amsterdam (1909) Slide credit: Mark Raat
  12. 12. Stats Amsterdam HisGis • 31,554 location points in historic city centre • 21,130 location points outside city centre • Streets + house numbers for all locations of Public Works 1909 map • https://hisgis.nl/projecten/amsterdam/ • Find out more at: https://amsterdamtimemachine.nl/ D I G I TA L H U M A N I T I E S L A B
  13. 13. Cinema locations and tram lines (1921) Based on slide by: Vincent Baptist, Julia Noordegraaf & Thunnis van Oort
  14. 14. Average yearly house rent with cinema locations Based on slide by: Vincent Baptist, Julia Noordegraaf & Thunnis van Oort
  15. 15. W I T H E R A S M U S U N I V E R S I T Y R O T T E R D A M R E F U G E E O R M I G R A N T ? Concepts and Words
  16. 16. Debates on the refugee crisis • From 2015 on, wider use of both ‘European refugee crisis’ and ‘European migrant crisis’ in the news and social media • “Framing labels” (Knoll, Redlawsk, & Sanborn, 2011) imply two different frames: • ‘Refugee’ – people fleeing conflict or persecution • ‘Migrant’ – improving economic situation • Mixed usage and mislabeling have implications for refugees, e.g., negative influence on perceptions of host countries D I G I TA L H U M A N I T I E S L A B
  17. 17. What’s in a name?  Framing matters • Framing through labeling of events and individuals: • Frames elicit and shape people’s interpretative activities (Goffman, 1974; Pan & Kosicki, 1993). • Influence receivers (Entman, 1993; Rohan, 2000; Chong & Druckman, 2007; Druckman, 2011) • Traditional media’s use of labels framing migration issues (Haynes, Devereaux, Breen, 2008; Horsti, 2007; Roggeband and Verloo, 2007) • Research on framing labels in social media is sparse D I G I TA L H U M A N I T I E S L A B
  18. 18. Impacts of social media • Social media influences emotions and behaviors and activities (e.g., Sobkowicz et al., 2012). • Social media effects: • Stimulates social interactions (Lillie 2008) • Commenting is not only a reaction to the media itself but a larger dialogue over broader issues (Madden et al. 2013) • Comments serve as a lens for public opinion (Kirk and Schill, 2011; Porter and Hellsten, 2014). • Mirror “real-life” communication behavior (Schultes et al., 2013) D I G I TA L H U M A N I T I E S L A B
  19. 19. Positivity vs. Negativity in social media D I G I TA L H U M A N I T I E S L A B
  20. 20. Refugee or migrant crisis? Labels, perceived agency, and sentiment polarity in online discussions • Six minute YouTube video published on Sept. 17, 2015: https://www.youtube.com/ watch?v=RvOnXh3NN9 • 46,313 user-generated comments and replies, up until October 22, 2016 • Sentiment analysis (SentiStrength) and topic modeling (LDA) on comments • 100% of tweets from Sept 4-5, 2015 (death of Aylan Kurdi) via Gnip/Sifter (N = ~370K), augmented by additional tweets across time (for MDS) • Socio-semantic networks (@user-to- #hashtag bipartite network), sentiment analysis (SentiStrength), regression models The Refugee/Migrant crisis dichotomy on Twitter: A network and sentiment perspective Nerghes,A., & Lee, J. (2018).The Refugee / Migrant Crisis Dichotomy onTwitter:A Network and Sentiment Perspective. In 10th ACM Conference onWeb Science (pp. 271– 280).Amsterdam,The Netherlands:ACM. https://doi.org/10.1145/3201064.3201087 Lee, J.-S. and Nerghes,Adina (2018). Refugee or migrant crisis? Labels, perceived agency, and sentiment polarity in online discussions. Social Media + Society, 4(3). https://doi.org/10.1177/2056305118785638 D I G I TA L H U M A N I T I E S L A B
  21. 21. • Sentiment ordering of immigrant (most negative) to migrant to refugee to Syrian may indicate a multidimensional mixture of: • Threat: actors have been portrayed as constituting a criminal threat to host societies • Agency: actors’ having relatively higher agency in crossing-borders • Permanence: whether or not actors are expected to permanently reside in a host country • Economic Cost: refers to the expectation of economic costs incurred by the presence of these actors in a host country • Negative labels worsened, demanding investigation into interplay with negative media reports (future work). Findings (Youtube) T H R E AT A G E N C Y P E R M A N E N C E C O S T S D I G I TA L H U M A N I T I E S L A B
  22. 22. Findings (Twitter) • #Refugee* is more positive and less intense than #Migrant* -> more sympathetic (H1) • Popular (and “influential”) users are less provocative and muted in sentiment (H2). • Lack of Intensity produces more retweeting (H3). • Negativity begets more retweeting than positivity (H4), but so does #Refugee* (strongly), which is more positive • Wealth of positivity sentiment seems to defy the negativity of events, despite the necessary use of negative words even in sympathy. • Users are not inclined to share emotionally nuanced or conflicting content 18 ~H2 ~H2 ?H4 ~H3 D I G I TA L H U M A N I T I E S L A B
  23. 23. Crossing Genres Image source: www.banksy.co.uk 
  24. 24. Background • Characters and relations are backbone of stories • Computational methods allow for scaling up network extraction and analysis • Relies on named entity recognition • Most work thusfar focuses on 19th and early 20th century novels • Research question: how do these tools perform on modern science fiction/fantasy novels? D I G I TA L H U M A N I T I E S L A B Image source: https://www.flickr.com/photos/yellowbacks/19708688368
  25. 25. Experimental setup • Collect 20 ‘old’ and 20 ‘new’ novels • Annotate first chapters for entities and relationships between entities (gold standard) • Run 4 named entity recognisers on the sets of ‘old’ and ‘new’ novels • Compare system outputs to gold standard annotations • Bonus: compare network structures Image source: delpher.nl D I G I TA L H U M A N I T I E S L A B Image source: https://cdn-images-1.medium.com/max/2400/1*QbCo9uE7jPbt1ttnMsqOog.jpeg
  26. 26. Why is fiction hard for NLP? • Fiction writers don’t have to abide by conventions: they can use language more creatively than newspaper journalists • mix languages • make up languages • use nicknames • Narratives written from first-person perspective confuse the software • For more information: Dekker, Niels, Tobias Kuhn, and Marieke van Erp. "Evaluating named entity recognition tools for extracting social networks from novels." PeerJ Computer Science 5 (2019): e189. D I G I TA L H U M A N I T I E S L A B Image source: https://steamuserimages-a.akamaihd.net/ugc/859477733475369907/F34770D6EFEC30A70A84BEFE93C2C522C0B4A902/
  27. 27. ChalaisChalais M. BonacieuxM. Bonacieux de M. Busignyde M. Busigny Houdiniere LaHoudiniere La John FeltonJohn Felton Bois-Tracy de Ma...Bois-Tracy de Ma... de M. Schombergde M. Schomberg LubinLubin Porthos MonsieurPorthos Monsieur la Harpe de Ruela Harpe de Rue RochellaisRochellais Richelieu deRichelieu de de Busigny Monsi...de Busigny Monsi... Milady ClarikMilady Clarik RochefortRochefort Grimaud MonsieurGrimaud Monsieur M. CoquenardM. Coquenard de Treville Mons...de Treville Mons... Mr. FeltonMr. Felton MontagueMontague dâArtagnan Mon...dâArtagnan Mon... Buckingham de Mo...Buckingham de Mo... de Monsieur Voit...de Monsieur Voit... Monsieur Bernajo...Monsieur Bernajo... III HenryIII Henry Monsieur Dessess...Monsieur Dessess... de Chevreuse Mad...de Chevreuse Mad... Donna EstafaniaDonna Estafania Lord DukeLord Duke Quixote DonQuixote Don Lorme de MarionLorme de Marion de Cahusac Monsi...de Cahusac Monsi... BazinBazin Chevalier Monsie...Chevalier Monsie... MusketeerMusketeer Constance Bonaci...Constance Bonaci... M. DessessartM. Dessessart GermainGermain de M. Cavoisde M. Cavois JudithJudith GasconGascon MousquetonMousqueton Monsieur AthosMonsieur Athos Duke MonsieurDuke Monsieur Charlotte BacksonCharlotte Backson BethuneBethune Planchet MonsieurPlanchet Monsieur Louis XIIILouis XIII Bonacieux MadameBonacieux Madame de Benserade Mon...de Benserade Mon... GervaisGervais MeungMeung Chesnaye LaChesnaye La Bonacieux Monsie...Bonacieux Monsie... ChrysostomChrysostom Wardes de De M.Wardes de De M. Coquenard Monsie...Coquenard Monsie... PatrickPatrick BerryBerry MandeMande Laporte M.Laporte M. de M. Laffemasde M. Laffemas Laporte MonsieurLaporte Monsieur Louis XIVLouis XIV AnneAnne de M. Tremouille...de M. Tremouille... NormanNorman de M. Bassompier...de M. Bassompier... IV HenryIV Henry Villiers GeorgeVilliers George BearnaisBearnais I CharlesI Charles PierrePierre monsieur Aramis ...monsieur Aramis ... JussacJussac DenisDenis GasconsGascons Coquenard MadameCoquenard Madame CrevecoeurCrevecoeur PicardPicard pope Popepope Pope de M. Trevillede M. Treville de Marie Medicisde Marie Medicis LorraineLorraine #N/A#N/A Cardinal MonsieurCardinal Monsieur FourreauFourreau BicaratBicarat Marie Michon MAR...Marie Michon MAR... Lord de WinterLord de Winter Milady de De Win...Milady de De Win... M. dâArtagnanM. dâArtagnan DukeDuke Messieurs PorthosMessieurs Porthos KittyKitty The Three Musketeers: F1 32 - 48
  28. 28. ChalaisChalais M. BonacieuxM. Bonacieux de M. Busignyde M. Busigny Houdiniere LaHoudiniere La John FeltonJohn Felton Bois-Tracy de Ma...Bois-Tracy de Ma... de M. Schombergde M. Schomberg LubinLubin Porthos MonsieurPorthos Monsieur la Harpe de Ruela Harpe de Rue RochellaisRochellais de Marie Medicisde Marie Medicis de Busigny Monsi...de Busigny Monsi... Milady ClarikMilady Clarik RochefortRochefort Grimaud MonsieurGrimaud Monsieur M. CoquenardM. Coquenard de Treville Mons...de Treville Mons... Commissary Monsi...Commissary Monsi... Mr. FeltonMr. Felton MontagueMontague Buckingham de Mo...Buckingham de Mo... de Monsieur Voit...de Monsieur Voit... M. DartagnanM. Dartagnan Monsieur Bernajo...Monsieur Bernajo... III HenryIII Henry Monsieur Dessess...Monsieur Dessess... de Chevreuse Mad...de Chevreuse Mad... Donna EstafaniaDonna Estafania Lord DukeLord Duke Quixote DonQuixote Don Lorme de MarionLorme de Marion de Cahusac Monsi...de Cahusac Monsi... BazinBazin Chevalier Monsie...Chevalier Monsie... MusketeerMusketeer M. DessessartM. Dessessart GermainGermain de M. Cavoisde M. Cavois JudithJudith Monsieur Dartagn...Monsieur Dartagn... GasconGascon MousquetonMousqueton Monsieur AthosMonsieur Athos Duke MonsieurDuke Monsieur Charlotte BacksonCharlotte Backson BethuneBethune Planchet MonsieurPlanchet Monsieur Louis XIIILouis XIII Milady de WinterMilady de Winter Bonacieux MadameBonacieux Madame de Benserade Mon...de Benserade Mon... GervaisGervais MeungMeung Chesnaye LaChesnaye La Bonacieux Monsie...Bonacieux Monsie... ChrysostomChrysostom Wardes de De M.Wardes de De M. Coquenard Monsie...Coquenard Monsie... PatrickPatrick Lord de De WinterLord de De Winter BerryBerry MandeMande Laporte M.Laporte M. Richelieu deRichelieu de GodeauGodeau Laporte MonsieurLaporte Monsieur Louis XIVLouis XIV AnneAnne de M. Tremouille...de M. Tremouille... NormanNorman de M. Bassompier...de M. Bassompier... IV HenryIV Henry Villiers GeorgeVilliers George de M. Laffemasde M. Laffemas BearnaisBearnais PierrePierre monsieur Aramis ...monsieur Aramis ... JussacJussac DenisDenis GasconsGascons CrevecoeurCrevecoeur PicardPicard pope Popepope Pope de M. Trevillede M. Treville de Monsieur Cavo...de Monsieur Cavo... LorraineLorraine Dangouleme DucDangouleme Duc #N/A#N/A Cardinal MonsieurCardinal Monsieur FourreauFourreau BicaratBicarat Marie Michon MAR...Marie Michon MAR... I CharlesI CharlesDukeDuke VilleroyVilleroy Messieurs PorthosMessieurs Porthos KittyKitty Bonacieux Consta...Bonacieux Consta... The Three Musketeers after rewriting d’Artagnan to Dartagnan
  29. 29. Performance fixes • Replace word names with generic names • Remove apostrophes from names • But: • Requires manual intervention • Doesn’t scale D I G I TA L H U M A N I T I E S L A B
  30. 30. 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  32. 32. Work in progress Historical Image Analysis (@MelvinWevers) Global Apple Pie (with Ulbe Bosma & Rebeca Ibáñez-Martîn) 18th century career mobility (DHLab + HI + DI) What makes or breaks an idea? (@AdinaNerghes)
  33. 33. Discussion • Humanities research studies the world’s complexity • Often at odds with what computers can deal with: • fluid concepts • changes over time • unstructured data • language variation • non-digital born data • KNAW HuC is working towards Cultural AI: The study, design and development of socio-technological AI systems that are implicitly or explicitly aware of the subtle and subjective complexity of human culture D I G I TA L H U M A N I T I E S L A B
  34. 34. Digital Humanities Lab History, Literary Studies, History of Science & Scholarship Social History Dutch Language & Culture http://dhlab.nl

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