1/20

Digitally Empowered Education in SSH

Kristoffer L Nielboknielbo@sdu.dk

knielbo.github.io

Dept. of History & SDU eScience CenterUniversity of Southern Denmark

2/20www.skynettoday.com/content/news/facebook-chatbot-language/

3/20

I’d like the books and the hats

I will take the books and hats

I really need the books and at least oneother object

Ok, you can have one book and one ball

If I can have all the books, I can leave youthe rest

Deal

You can have the ball if I can haveeverything else

No can’t do that the ball is not going togive me anything

I need the hats and you can have the rest

Deal

Great! Thank you!

https://github.com/facebookresearch/end-to-end-negotiator

4/20

i can i i everything else . . . . . . . . . . . . . .

balls have zero to me to me to me to me to me to me to me to me to

you i everything else . . . . . . . . . . . . . .

balls have a ball to me to me to me to me to me to me to me

i i can i i i everything else . . . . . . . . . . . . . .

balls have a ball to me to me to me to me to me to me to me

i . . . . . . . . . . . . . . . . . . .

balls have zero to me to me to me to me to me to me to me to me to

you i i i i i everything else . . . . . . . . . . . . . .

balls have 0 to me to me to me to me to me to me to me to me to

you i i i everything else . . . . . . . . . . . . . .

balls have zero to me to me to me to me to me to me to me to me to

5/20

compromise0: IPRON willAUX takeVERB theDET booksNOUN andCONJ hatsNOUN

compromise1: YouPRON canAUX haveVERB theDET ballNOUN ifSCONJ IPRON canAUX

haveVERB everythingNOUN elseADJ

stubborn: IPRON getVERB allDET theDET ballsNOUN ?PUNCT

singularity: ballsNOUN haveVERB zeroADJ toADP mePRON toADP mePRON toADP mePRON

toADP mePRON toADP mePRON toADP mePRON toADP mePRON toADP mePRON toPART

compromise0 compromise1 stubborn singularityH(X ) 2.53 (1.16) 2.3 (1.35) 2.59 (0.84) 1.62 (0.51)TTR 0.92 (0.09) 0.94 (0.07) 0.96 (0.09) 0.5 (0.27)

6/20

7/20

8/20

9/20

Core observations

– impossible without digitization– cultural data require culture analytics– qual-quant distinction is no longer valid– scaling requires automation– there is no way around basic programming

humanities research and education need a define our human-centered informatics

10/20

– the data deluge is transforming knowledge discovery and understanding in everydomain of human inquiry

– knowledge discovery depends critically on advanced computing capabilities

a large part of these data are soft and unstructured

– to get additional value from these data, faculties of humanities must becomecomputationally and data literate

11/20

– number of research publications alone makes computational literacy a necessity forthe humanities scholar

– publications related to Gospel of Marc (KJV) > 50K, ∼ 16,500 words in 16 chp. on 11 p.

– plus a massive increase in digitized cultural heritage databases (libraries, archieves,museums)

12/20

13/20

14/20

Computational Literacy|Programming & Analytics

– every knowledge intensive organization has to break the learning curve, but certainsectors are more challenged

– co-develop with the eScience Center and other resources @ SDU

– promote a common language and import best practice from software development

15/20

Computational Literacy|Programming & Analytics

GUI → CLI

- novice-friendly visual approach to computer interaction w. a fast learning curve ERROR

- expert-friendly text-based approach to computer interaction w. ++freedom VALID

- CONFLICT break the learning curve through training intensive, non-intuitive, andspecialized tools

- in research, we try to solve this conflict by establishing small, semi-autonomouseScience units that intervene in (humanities) research

16/20

Literary Studies|Sentiment Analysis

0 1000 2000 3000 4000 5000 6000−5

0

5

10

Time

Sentim

ent

Madame Bovary

(a) Original t = L/400 t = L/10

0 1000 2000 3000 4000 5000 6000−1

−0.5

0

0.5

1

Time

Sentim

ent

(b)

filtered (t = L/10)

filtered (t = 3L/8)

Figure: sentiment analysis and adaptive filteringreconstructs narrative vectors that reflect thereader experience. Particular fractalscaling-range, 0.6 < H ≤ 0.8, indicates literaryoptimality.

0 2 4 6 8 10 12−2

0

2

4

6

Hs=0.57

Hl=0.74

log2w

log

2F

(w)

17/20

History|Predictive Causality & Slow Decay

– historians and media researchers theorize about the causal dependencies betweenpublic discourse and advertisement

– time series analysis of keyword frequencies (from seedlists) indicated that for somecategories ‘ads shape society’, while other categories merely ‘reflect’

– advertisements show a faster decay (on-off intermittant behavior) than publicdiscourse (long-range dependencies)

Wevers, M., Nielbo, K. L., & Gao, J. (in review). Tracking the Consumption Junction: Temporal Dependencies in Dutch NewspaperArticles and Advertisements.

18/20

8 10 12

2

3

4

5

log10

GDP

log

10 n

o.

of

eve

nts

r = 0.81

(a) 1985

8 10 12

1

2

3

4

5

log10

GDP

log

10 n

o.

of

eve

nts

r = 0.68

(b) 1995

9 10 11 12 13

2

3

4

5

log10

GDP

log

10 n

o.

of

eve

nts

r = 0.69

(c) 2005

9 10 11 12 13

4

5

6

7

log10

GDP

log

10 n

o.

of

eve

nts

r = 0.8

(d) 2015

1980 1985 1990 1995 2000 2005 2010 20150.6

0.7

0.8

0.9

year

Pe

ars

on

co

rre

latio

n r (e)

15 20 25 30 35 40 4525

30

35

40USA

GBRLVA

ESPGRCPRT

AUSNZL

ITACAN

IRL

POLCHEFRA DEU

HUN

LUX SWEAUTNLD

SVKBELFIN

DNKNOR

r(Total)= 0.616

r(Gini≥26.5%)= 0.694

Domestic events(%)

Gin

i co

eff

icie

nt(

%)

1980 1985 1990 1995 2000 2005 2010 201515

20

25

30

35

40

45

Time

Dom

estic e

vents

(%)

USA IRL GBR NLD ITA

DEU DNK FIN FRA SWE Median

Figure: Event counts in the GDELT databasereflect economic and political dynamics

Gao J., Ma M., Liu B., Nielbo K.L., Roepstorff A., Tangherlini T., Roychowdhury V. (in review) Brexit and Trump Presidency: werethey black swan events or inevitable?

19/20

EMOTION|Grundtvig

– early phase: negative affective tone– late phase: positive affective tone– inverse relation → state incongruent writer

– emotional state Granger-causes creative state → dostoyevskian trope

20/20

THANK YOU

knielbo@sdu.dk

knielbo.github.io

& credits toMax R. Echardt and Katrine F. Baunvig, datakube, University of Southern Denmark, DK

Jianbo Gao and Bin Liu, Institute of Complexity Science and Big Data, Guangxi University, CHNMelvin Wevers, DH Lab, KNAW Humanities Cluster, NL

Culture Analytics @ Institute of Pure and Applied Mathematics, UCLA, US