This paper looks at the reality of developing and implementing a bespoke tagging system that could be effective for users yet simple enough to use for staff who are not specialist classifiers, including human issues for staff. Accompanies paper at Museums and the Web 2011 http://bit.ly/KZiudD

1. And re w Le wis S e nior We b C onte nt M anage r, Victoria and Alb e rt M u s e u m
Linked In h ttp :/ b it.ly/ O p AN F
/ h
M ixing It U p :
D e ve lop ing and im p le m e nting an in-h ou s e
tagging s ys te m for a conte nt-rich we b s ite
M u s e u m s and th e We b
7 Ap ril 201 1

2. And re w Le wis
 S e nior We b C onte nt M anage r at th e
Victoria and Alb e rt M u s e u m Lond on, s ince
2008
 R e s p ons ib le for te am m anaging conte nt
on we b s ite
 Inform ation s cie nce training with p u b lic
lib rary e le ctronic s e rvice s b ackgrou nd

3. Th is s e s s ion is h op ing to give you …
 A gre ate r u nd e rs tand ing of wh at tagging
can d o and h ow th at m igh t b e u s e fu l for
you .

4. F orm at of th is s e s s ion
 Backgrou nd - wh at we we re trying to d o,
and h ow we ap p roach e d it
 An au d ie nce e xe rcis e in tagging...

5. Th is s e s s ion d is cu s s e s
 Brie f b ackgrou nd to V&A we b s ite
re d e ve lop m e nt
 Atte m p ts to cros s -conne ct vis itors with
conte nt th e y m igh t not re alis e we h ave
 D e ve lop ing a tagging s ys te m for s im p le
s u b j ct clas s ification
e
 Th e h u m an is s u e s arou nd tagging conte nt

6. Backgrou nd - re d e ve lop ing th e V&A we b s ite
 R ationale
– To im p rove s ite vis u ally (e as y b it)
– To m ove to organis ation b y u s e r’s inte re s ts
not b y inte rnal d e p artm e ntal s tru ctu re
– To u p d ate s ite to b e re s p ons ive to typ ical
googling/ ocial-re com m e nd ation b e h aviou r of
s
we b u s e rs
– Move toward s op e n s ou rce and inte gration of
u s e r-ge ne rate d conte nt and we b s ocial m e d ia

7. C u rre nt we b s ite …

8. H ie rarch ie s
8
b e ta.vam .ac.u k

9. H ie rarch ie s
9

10. H ie rarch ie s
h e lp
10
b e ta.vam .ac.u k

11. Bu t...
Bu t…

12. H ow d o vis itors land on ou r we b s ite ?
S o u rce : G o ogle An alytics fo r V&A s ite : 1 Janu ary – 31 D e ce m b e r 201 0

13. C lu s te ring arou nd u s e rs ’ s u b j cts of inte re s t
e

14. H ow d o vis itors land on ou r we b s ite ?
S o u rce : G o ogle An alytics fo r V&A s ite : 1 Janu ary – 31 D e ce m b e r 201 0

15. H ow d o vis itors land on ou r we b s ite ?

18. 3 le ve ls of control…

19. 3 le ve ls of control
 E ditorial – ab ility to s p e cify an article m u s t
ap p e ar u nd e r a s u b j ct clu s te r
e
 S emi-automatic – ab ility to d e fine th e ke y
s u b j ct of th e article or s u b j ct clu s te r as th e
e e
b as is for clu s te ring conte nt
 A utomatic – s e arch logic analys e s th e te rm s in
th e conte nt and u s e s algorith m s to m atch it with
oth e r conte nt

20. Au tom atic and
s e m i-au tom atic
Au tom atic and
s e m i-au tom atic
E d itorially s e le cte d conte nt
Au tom atic and
s e m i-au tom atic

21. Tagging…

22. 6 tagging fie ld s ...
§ “ pers on” to re fle ct th at vis itors wou ld b e looking for inform ation ab ou t
s p e cific artis ts , d e s igne rs , h is torical and fictional figu re s
§ “ place” to re fle ct vis itors inte re s t in art and d e s ign as s ociate d with
p articu lar locations
§ “ technique” to s u p p ort vis itors trying to u nd e rs tand artis tic and d e s ign
p roce s s e s
§ “ period/s tyle” to re fle ct an inte re s t in artis tic m ove m e nts or s ignificant
nam e d h is torical p e riod s
§ “ date” to re fle ct th at vis itors m ay b e inte re s te d in work p rod u ce d at a
p articu lar p oint in h is tory
§ “ free text” – th is was inclu d e d to allow fle xib ility and b e ab le to e m p loy
te rm s not cove re d by th e m ore s p e cific cate gorie s

23. A lot of s p re ad s h e e ts ...
23

24. S cale …

25. Th e p roce s s of tagging conte nt
– Au d it re ve ale d ove r 4,000 p age s on th e m ain s ite
– Tagge d ind ivid u ally b y re ad ing th e p age and
d e cid ing th e m ain s u b j ct(s )
e
– Pos s ib le s u b j ct clu s te rs note d at th e s am e tim e
e
– At th e tim e , s taff cou ld not s e e th e e ffe ct of tagging,
as s ys te m was not b u ilt
– R u le s d e ve lop e d as a grou p e xe rcis e with d aily
fe e d b ack and d is cu s s ion as tagging p rogre s s e d

26. Id e ntifying clu s te rs ...
26

27. You r tu rn…

28. O p e ra tagging

29. h ttp :/ b e ta.vam .ac.u k/ age / / e m b e rs h ip
/ p m m
h ttp :/ b e ta.vam .ac.u k/ age / kim ono
/ p k/

30. Th ings to take away...
 Tagging offe rs a p ragm atic m e th od for aggre gation
with ou t cu m b e rs om e s ch e m as
 It is lab ou r inte ns ive
 C las s ification is not as s im p le as it ap p e ars . Tagging
ne e d s b alance b e twe e n accu racy and “good e nou gh ”
 Tagging is ve ry s u b j ctive
e
 Pe op le as s ign im p ortance accord ing to th e ir knowle d ge
of a s u b j ct
e
 D e ve lop ing tagging with ou t s e e ing th e re s u lts cre ate s
anxie ty

31. e m ail: a.le wis @ vam .ac.u k
And re w Le wis
Linke d In: h ttp :/ b it.ly/ O p AN F
/ h
h ttp :/ b e ta.vam .ac.u k
/
Th ank you
M u s e u m s and th e We b
7 Ap ril 201 1