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/
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. 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
12. 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
14. 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
15. Cinema locations and tram lines (1921)
Based on slide by: Vincent Baptist, Julia Noordegraaf & Thunnis van Oort
16. Average yearly house rent with cinema locations
Based on slide by: Vincent Baptist, Julia Noordegraaf & Thunnis van Oort
17. 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
18. 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
19. 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
20. 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
22. 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
23. • 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
24. 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
26. 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
27. 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
28. 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/
29. 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
30. 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
31. 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
32. JosethJoseth
Harys SerHarys Ser
BrackensBrackens
Lord RobbLord Robb
CoholloCohollo
Piper Ser MarqPiper Ser Marq
HullenHullen
Tommen PrinceTommen Prince
Trant Meryn SerTrant Meryn Ser
Hightower Ser GeroldHightower Ser Gerold
Lord VanceLord VanceDareonDareon
Arya HorsefaceArya Horseface
Lord HornwoodLord Hornwood
Robert BaratheonRobert BaratheonCotter PykeCotter Pyke
Caron Lord BryceCaron Lord Bryce
EliaElia
Stark SansaStark Sansa
Mott MasterMott Master
AggoAggo
Rodrik Cassel SerRodrik Cassel Ser ThorosThoros
LyannaLyanna
Ser DonnelSer Donnel
NymeriaNymeria
SherrerSherrer
Tarly SamTarly Sam
JhiquiJhiqui
Alyssa ArrynAlyssa Arryn
JyckJyck
YorenYoren
Frey LadyFrey Lady
Rayder ManceRayder Mance
PypPyp
Manderly Ser WylisManderly Ser Wylis
ChellaChella
JhogoJhogo
ChiggenChiggen
Dontos SerDontos Ser
Bronze Yohn RoyceBronze Yohn Royce
ChettChett
VisenyaVisenya
Cassel JoryCassel Jory
GrennGrenn
Lord SlyntLord Slynt
Hal MollenHal Mollen
Ned StarkNed Stark
Stark BrandonStark Brandon
MikkenMikken
Greyjoy BalonGreyjoy Balon
MorrecMorrec
TomardTomard
DanwellDanwell
Mya StoneMya Stone
HeartsbaneHeartsbane
Jaremy Ser RykkerJaremy Ser Rykker
Egen Ser VardisEgen Ser Vardis
GodwynGodwyn
Castle BlackCastle Black
Lord Dondarrion BericLord Dondarrion Beric
Brynden BlackfishBrynden Blackfish
Maester LuwinMaester Luwin
Maester AemonMaester Aemon
CravenCraven
MordMord
MattMatt
Clegane SandorClegane Sandor
ShaeShae
HarrenhalHarrenhal
Lord Nestor RoyceLord Nestor Royce
PentoshiPentoshi
ToadToad
PortherPorther
Lord lord TyrionLord lord Tyrion
MagoMago
Vargo HoatVargo Hoat
RickonRickon
EroehEroeh
Lord ArrynLord Arryn
QuaroQuaro
Lord PiperLord Piper
Lysa Lady ArrynLysa Lady Arryn
BraavosiBraavosi
MattharMatthar
Bracken Jonos LordBracken Jonos Lord
Lord StewardLord Steward
Manderly Ser WendelManderly Ser Wendel
TregarTregar
TimettTimett
Santagar Ser AronSantagar Ser Aron
Barristan Selmy SerBarristan Selmy Ser
Payne Ser IlynPayne Ser Ilyn
Boy MoonBoy Moon
Perwyn SerPerwyn Ser
Lord Mallister JasonLord Mallister Jason
Samwell TarlySamwell Tarly
Poole VayonPoole Vayon
JoffteyJofftey
BethBeth
GaredGared
MoreoMoreo
Whent Oswell SerWhent Oswell Ser
Forel SyrioForel Syrio
DanyDany
KurleketKurleket
GreatjonGreatjon
Lannister TyrionLannister Tyrion
Ser Moore MandonSer Moore Mandon
Lord WymanLord Wyman
HardinHardin
DorneDorne
Lord JonLord Jon
Stannis Baratheon LordStannis Baratheon Lord
JerenJeren
UlfUlf
Fat TomFat Tom
Jaime Ser LannisterJaime Ser Lannister
Ogo KhalOgo Khal
Moat CailinMoat Cailin
Cassel MartynCassel Martyn
Alliser Ser ThorneAlliser Ser Thorne
FarlenFarlen
Lord RobertLord Robert
LysLys
Lord RowanLord Rowan
Jeyne PooleJeyne Poole
TyroshiTyroshi
ConnConn
MaegorMaegor
HaggoHaggo
ValeVale
Edmure Ser TullyEdmure Ser Tully
HighgardenHighgarden
GageGage
Hill HornHill Horn
CorattCoratt
Heddle MashaHeddle Masha
Maege MormontMaege Mormont
Lady Catelyn StarkLady Catelyn Stark
CaynCayn
Ben StarkBen Stark
MarillionMarillion
Lady MormontLady Mormont
KingKing
Robert ArrynRobert Arryn
GendryGendry
Xho JalabharXho Jalabhar
KhaleesiKhaleesi
Lord Baratheon RenlyLord Baratheon Renly
AlynAlyn
Lord Baelish PetyrLord Baelish Petyr
Lady SansaLady Sansa
Mirri Maz DuurMirri Maz Duur
Lord Frey WalderLord Frey Walder
FatherFather
Ser Addam MarbrandSer Addam Marbrand
Hugh SerHugh Ser
Old NanOld Nan
LharysLharys
JacksJacks
Rhaegar TargaryenRhaegar Targaryen
Joffrey PrinceJoffrey Prince
Boros Ser BlountBoros Ser Blount
Vance KarylVance Karyl
JoffJoff
Arthur Dayne SerArthur Dayne Ser
Mordane SeptaMordane Septa
Ser Tallhart HelmanSer Tallhart Helman
Lord Tytos BlackwoodLord Tytos Blackwood
Tywin Lord LannisterTywin Lord Lannister
Yi TiYi Ti
Jen BenJen Ben
HalderHalder
ShaggaShagga
Arryn JonArryn Jon
DolfDolf
BaelorBaelor
GunthorGunthor
Tyrell Ser LorasTyrell Ser Loras
Lannister Ser KevanLannister Ser Kevan
Stevron Frey SerStevron Frey Ser
Tanda LadyTanda Lady
Raymun Darry SerRaymun Darry Ser
ShaggydogShaggydog
Lord Tully HosterLord Tully Hoster
Arys SerArys Ser
Flowers JaferFlowers Jafer
Willis Ser WodeWillis Ser Wode
DawnDawn
HewardHeward
Willem DarryWillem Darry
FogoFogo
MalleonMalleon
WillWill
Rhaggat KhalRhaggat Khal
MycahMycah
JaggotJaggot
Flement Brax SerFlement Brax Ser
UmarUmar
Robar SerRobar Ser
NaerysNaerys
CheykCheyk
Tobho MottTobho Mott
Benjen StarkBenjen Stark
MohorMohor
LittlefingerLittlefinger
Lord TyrellLord Tyrell
Brynden Ser TullyBrynden Ser Tully
HaliHali
MyrcellaMyrcella
StivStiv
Othell YarwyckOthell Yarwyck
Greyjoy TheonGreyjoy Theon
IrriIrri
Maester PycelleMaester Pycelle
Grey WindGrey Wind
Quorin HalfhandQuorin Halfhand
JaehaerysJaehaerys
Lord CerwynLord Cerwyn
ClydasClydas
RakharoRakharo
DywenDywen
Magister IllyrioMagister Illyrio
TorrhenTorrhen
Aegon TargaryenAegon Targaryen
Bowen MarshBowen Marsh
Daryn HornwoodDaryn Hornwood
RiverrunRiverrun
Clegane Gregor SerClegane Gregor Ser
Snow JonSnow Jon
RastRast
Aerys TargaryenAerys Targaryen
Drogo KhalDrogo Khal
Viserys TargaryenViserys Targaryen
QothoQotho
Whent LadyWhent Lady
Hobb Three-FingerHobb Three-Finger
DothrakiDothraki
Royce Ser AndarRoyce Ser Andar
Karyl SerKaryl Ser
HakeHake
LanceLance
HosteenHosteen
Mace TyrellMace Tyrell
Lord HunterLord Hunter
Hallis MollenHallis Mollen
Dothrak VaesDothrak Vaes
Daeren TargaryenDaeren Targaryen
Lord LeffordLord Lefford
VolantisVolantis
Glover GalbartGlover Galbart
RhaegoRhaego
Bolton RooseBolton Roose
Catelyn TullyCatelyn Tully
Lannister CerseiLannister Cersei
JossJoss
Waymar Ser RoyceWaymar Ser Royce
Lothor BruneLothor Brune
Lord Tarly RandyllLord Tarly Randyll
Derik LordDerik Lord
Jared Frey SerJared Frey Ser
TyroshTyrosh
Ser Swann BalonSer Swann Balon
Lord VarysLord Varys
BranBran
Harrion KarstarkHarrion Karstark
JhaqoJhaqo
DoreahDoreah
HaiderHaider
bushbush
Janos SlyntJanos Slynt
Brothers MoonBrothers Moon
Arya StarkArya Stark
Daenerys TargaryenDaenerys Targaryen
Corbray Lyn SerCorbray Lyn Ser
HodorHodor
Robett GloverRobett Glover
HarwinHarwin
Lord Karstark RickardLord Karstark Rickard
BronnBronn
Hobber SerHobber Ser
Khal JommoKhal Jommo
Horas SerHoras Ser
Lord MormontLord Mormont
DesmondDesmond
StarksStarks
Robb StarkRobb Stark
Lord Hand lordLord Hand lord
AlbettAlbett
Noye DonalNoye Donal
Jorah Ser MormontJorah Ser Mormont
33. CoholloCohollo
EliaElia
AggoAggo
JhiquiJhiqui
ChellaChella
JhogoJhogo
ShaeShae
PentoshiPentoshi
MagoMago
Vargo HoatVargo Hoat
EroehEroeh
QuaroQuaro
rdrd
TimettTimett
DanyDany
annister Tyrionannister Tyrion
DorneDorne
UlfUlf
Ogo KhalOgo Khal
LysLys
ConnConn
HaggoHaggo
HighgardenHighgarden
KingKing
KhaleesiKhaleesi
Mirri Maz DuurMirri Maz Duur
Rhaegar TargaryenRhaegar Targaryen
Vance KarylVance Karyl
Yi TiYi Ti
ShaggaShagga
DolfDolf
GunthorGunthor
Lannister Ser KevanLannister Ser Kevan
Raymun Darry SerRaymun Darry Ser
FogoFogo
Rhaggat KhalRhaggat Khal
Flement Brax SerFlement Brax Ser
UmarUmar
NaerysNaerys
CheykCheyk
Lord TyrellLord Tyrell
IrriIrri
RakharoRakharo
Magister IllyrioMagister Illyrio
Aegon TargaryenAegon Targaryen
Drogo KhalDrogo Khal
Viserys TargaryenViserys Targaryen
QothoQotho
DothrakiDothraki
Karyl SerKaryl Ser
Dothrak VaesDothrak Vaes
Daeren TargaryenDaeren Targaryen
Lord LeffordLord Lefford
RhaegoRhaego
Lannister CerseiLannister Cersei
JossJoss
Derik LordDerik Lord
TyroshTyrosh
JhaqoJhaqo
DoreahDoreah
MoonMoon
Daenerys TargaryenDaenerys Targaryen
onnonn
Khal JommoKhal Jommo
Lord MormontLord Mormont
Robb StarkRobb Stark
Jorah Ser MormontJorah Ser Mormont
34. 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)
35. 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