Do Museums Worldwide form a true Community on Twitter? Museum Twitter ecosystem (Social Network Analysis).

Alex Espinós ~ alex@lamagnetica.com
Marc Espín ~ marc@lamagnetica.com

___
Do Museums
Worldwide form
a true Community
on Twitter?
Some insights on the museum Twitter ecosystem
through Social Network Analysis and Network Science.

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The Museum ecosystem on Twitter

The study

Why this study?
Questions we want to answer
Methodology

Introduction

Introduction

Conclusions

1


Conclusions

The study

Introduction

> Why this study?


> Why this study?

The study
Conclusions

The new paradigm
of communication that
Social Networks have
brought

Introduction

Everybody talks about things like:


> Why this study?

The study

Knowledge and
experience sharing

Conclusions

The new paradigm
brought

Introduction



> Why this study?

Information flow

The study

Knowledge and
experience sharing

Conclusions

The new paradigm
brought

Introduction



> Why this study?


Information flow

Introduction
The study

Knowledge and
experience sharing

Conclusions

The new paradigm
brought

User feedback


> Why this study?

Conclusions

The study

Introduction

OK, but is it true for museums?
Or is it still an unrealized possibility?


Conclusions

The study

Introduction

> Questions we want to answer



Conclusions

The study

Introduction

The study is focused to offer insights
over questions such as:



The study

Do the worldwide museums form a community on Twitter?
Or several different communities?
And then: which is the main criterion that explains the community structure:
topic, language or country?

Conclusions

··
··
··

Introduction




The study

··
··
··

Do the Twitter communities seem to mimic patterns of offline relationships?
Which are the communities? Which are the key players in each community?
Does the small world principle apply?

Conclusions

··
··
··

Introduction




The study

··
··
··
··
··

Do the Twitter communities seem to mimic patterns of offline relationships?
Which are the communities? Which are the key players in each community?
Does the small world principle apply?
And the strength of weak ties? To what extend?
Which are the most influential museums, not in terms of followers,
but in terms of influence within museums?

Conclusions

··
··
··

Introduction



Conclusions

The study

Introduction

> How?


> How?

Conclusions

The study

Introduction

We have analysed the Twitter talk among
museums worldwide, using
SNA (Social Network Analysis) techniques.


> How?

··

We are starting such a study with one Barcelona Museum.
The study

Later on we will focus on some case studies about the relationship of museums and
their followers. This involves a much heavier use of the Twitter API.

Conclusions

··

Introduction

We have analysed the Twitter talk among
museums worldwide, using
SNA (Social Network Analysis) techniques.


> How?

We have used the museum list from
Museum Analytics*:

Conclusions

*museum-analytics.org
Thank you to INTK team!

The study

Introduction

1.789 museums with a Twitter account


> How?

The study

* he final study will include data from a 12-month period.
T
Now we are showing results based on 9 months of data.

Conclusions

All their tweets from April 2013
123.972 interactions among museums
29.061 unique interactions

Introduction

And using the Twitter API we have got:


Communities
UK
Italy
Spain

The study

The big picture
The core graph
Country/language
UK Key role

Introduction

The study

Conclusions

2


Conclusions

The study

Introduction

The big picture

Conclusions

The study

Introduction


Conclusions

The core graph

The study

Introduction


The study

The core graph

Conclusions

These are the 400 most relevant museums
(relating to their importance in the graph
–i.e. in the Twitter talk among museums)

Introduction


Netherlands

USA

Germany

United Kingdom

Spain
Conclusions

ountry is
C
the key factor for explaining
the graph structure

Mexico

The study

Introduction

France

Netherlands

USA

Germany

There’s a clear country grouping in the
interactions between the museums.

The study

Mexico

Spain
Conclusions

ountry is
C
the key factor for explaining
the graph structure

Introduction

Apart from the labeled countries
there are important clusters for Canada,
Australia and New Zealand placed
France
between USA and UK.
And a Swedish one placed roughly
between Netherlands, Germany and
France.
United Kingdom

The study

K museums
U
have a Key role

Conclusions

Several UK museums are key players
in the spread of information between
Continental Europe ad USA museums.

Introduction


United states 28,3%
Unknown 18,24%
United Kingdom 10,69%
Netherlands 10,06%
Germany 9,43%
Spain 7,55%
France 5,03%

Mexico 4,4%
Canada 2,52%
Australia 1,89%
Austria 0,63%
Italy 0,63%
New Zealand 0,63%

Conclusions

ommunities, countries
C
and some key players

The study

Introduction



Mexico 4,4%
Canada 2,52%
Australia 1,89%
Austria 0,63%
Italy 0,63%
New Zealand 0,63%

The study

United states 28,3%
Unknown 18,24%
Netherlands 10,06%
Germany 9,43%
Spain 7,55%
France 5,03%

Conclusions

C

Introduction

Community structure is almost
the same as country grouping:
Twitter relationships mimic offline
relationships.
We are not getting the most
out of Twitter.


US community is the biggest,
but UK museums have a central position.
Speaking the world language is a clear advantage.
4 key US players:
@museummodernart, @metmuseum,
@guggenheim, @gettymuseum

Mexico 4,4%
Canada 2,52%
Australia 1,89%
Austria 0,63%
Italy 0,63%
New Zealand 0,63%

The study

United states 28,3%
Unknown 18,24%
Netherlands 10,06%
Germany 9,43%
Spain 7,55%
France 5,03%

Conclusions

C

Introduction

3 key British players:
@britishmuseum, @sciencemuseum, @v_and_a


No Italian community,
just a couple of scattered points.

Mexico 4,4%
Canada 2,52%
Australia 1,89%
Austria 0,63%
Italy 0,63%
New Zealand 0,63%

The study

United states 28,3%
Unknown 18,24%
Netherlands 10,06%
Germany 9,43%
Spain 7,55%
France 5,03%

Conclusions

C

Introduction

The order of European countries
does not seem to reflect the number
of their “offline” museum visitors:
UK, Netherlands, Germany, Spain,
France, Italy.

London 76,98%
None 11,11%
Edinburgh
5,56%
Casablanca
3,97%
Amsterdam
1,59%
Pacific Time (US Canada) 0,79%

Conclusions

ountry communities 1:
C
UK

The study

Introduction



The study

London 76,98%
None 11,11%
Edinburgh
5,56%
Casablanca
3,97%
Amsterdam
1,59%
Pacific Time (US Canada) 0,79%

Conclusions

ountry communities 1:
C
UK

Introduction

The Scottish cluster on the top.
Another example of offline relationships
replicated in the online environment.

Conclusions

Italy

The study

Introduction



The study

Italy

Conclusions

There are some isolated points:
Italian museums unrelated
to other Italian.

Introduction

We have gathered the Italian museums
from the whole graph. Most of them
are out of the core graph.


Language

Conclusions

Spain

The study

Introduction

Modularity


Language

Even inside the Spanish group,
the structure reflects the main language
used by different museums on Twitter:
Spanish, Catalan, and English.

Conclusions

Spain

The study

Introduction

Modularity


The study

Conclusions
How can this study help your museum?
#MTF2014 at Twitter

Introduction

Conclusions

Conclusions

3


Conclusions

The study
Conclusions

Most museums are not profiting the opportunities Twitter offer.

Introduction

››3.500 museums at museum-analytic.org
››1.800 museums at Twitter
››400/500 museums really engaged
with other museums


Conclusions

The study
Conclusions

1. ut there is a significant flow of information. (Small world principles applies)
B
Country is the main factor for explaining community structure
2. Not language: US and UK form different communities, so do Spain and Mexico
3. nd not topic: there is not an archaeological museums community, or a contemporary art
A
community… That would have reflected a maturity in Twitter use.

Introduction

There is not a worldwide Twitter community,
not even among those 400/500 museums.


Conclusions

The study
Conclusions

(Structural holes)

Introduction

There is a small set of key players
in the spread of information
and in the relationship among
museums worldwide


Conclusions

The study

Introduction




·· etect key players, analyse how they work on Twitter,
D
relate to them.

The study
Conclusions

·· onnecting the main component with your local or
C
topic-based community can be the first step.
Be bilingual! (at least).

Introduction

·· ecame a key player in your area (country, region, topic),
B
then go bigger.



The study
Conclusions

·· tay tuned for the final paper, with more detailed graphics,
S
museum lists, etc.

Introduction

·· se SNA approach to improve your work at Twitter,
U
your communication campaigns and the relationship
with your followers.


Conclusions

The study

Introduction

#MTF2014 at Twitter

MTF2014 at Twitter
#

Conclusions

The study

Introduction


MTF2014 at Twitter
#

The study
Conclusions

We will write a post next week about
the hashtag #mwf2014:
who has used it, the mention and RT
map about the conference, etc.

Introduction



Thanks!

The study
Conclusions

We want to partner with a museum for working a case study
analysing the relationship with its Twitter followers.
Are you interested? Contact us.

Introduction

For asking the final paper (April) contact us at:
alex@lamagnetica.com
@lamagnetica

Do Museums Worldwide form a true Community on Twitter? Museum Twitter ecosystem (Social Network Analysis).

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