Presentation for the talk that Alex Espinós, co-founder of LaMagnética, gave at the 17th edition of Data Beers BCN on Thursday, July 19th, 2018

1. Museums on Twitter
Using data to understand
community structure and growth
July 19th, 2018 – Antiga Fàbrica Estrella Damm
Alex Espinós
(@lamagnetica)

2. 1
Ø 10.000 followers
Ø 200 retweets
Ø 500 mentions
We needed a
different approach.
A community looks like this: … but we describe it
saying:

3. 2
Because the data is public and we can gather
it through its ‘generous’ API.
This allows deeper insights, Twitter is a lab to
test ideas, strategies, etc. In Facebook we can
see the outcome of our strategies but not
how the phenomenon takes place.
The SNA concepts and mechanisms we are
going to show can be applied –properly
adapted- to most Social Networks.
Why do we
focus on Twitter?

4. 3
Basic data gathering procedure for analysing the
community around a museum:
1. We get the list of its followers and the profiles
the museum is following.
2. We monitor all mentions and retweets to
and by the museum. Usually for several months,
to overcome the limits of Twitter Search API.
How do we analyse communities
and growth on Twitter?
3. For all users that have mentioned or retweeted the museum
or have been mentioned or retweeted by the museum, we
gather their timeline within the timespan of the analysis.
4. We check cross references between this users’ set.
Case study: London’s V&A Museum
§ Over 500,000 users.
§ Over 18,000 engaged users during the 7-month timespan.
§ 50+ million tweets.
§ A 18,000 x 18,000 matrix.

5. 4
When analysing followers growth we have
found that recommendations, mentions and
retweets are the main source of new follower
acquisition:
Ø They are based on same network
dynamics feature, the triadic closure.
Ø They provide 70-90% of the
new followers for museums.
1st key result
Triadic closure is the main source
for most of the Social Media growth
and information spread for museums
1st key result:
Triadic closure

6. 5
If two people are connected through a third person,
chances are they can get connected in the future.
Triadic closure is a broader concept, but we are going
to focus mainly on the transitivity of the follow relationship:
1st key result1st key result:
Triadic closure
if A follows B,
and B follows a C,
it is likely A will follow C.

7. 6
We can relate most of the followers’ growth to engaged users. In
our study, from 68 to 95% depending the museum.
The actual influence, the spread of information and growth
chances are provided by a subset of your followers:
those who really engage with you.
Engaged followers are those with whom you have an actual
(online) relationship through mentions or retweets.
This result should turn social media strategies
from focusing on the total number of followers
to focusing on engagement and on followers’ diversity.
2nd key result:
Followers vs. Engaged followers

8. 7
The museum is going to get most of its new followers
within the set of users that are connected to the museum
through a path of length 2.
Each mention and retweet creates
possibilities for triadic closure
(i.e. For acquiring new followers, for information spread).
Yellow: the museum
Blue: museum’s followers
Green: followers’ followers
Mentions and retweets

9. 8

10. 9

11. 10
Community analysis
Loosely defined a community is a group of users that have stringer ties inside the group
than with the rest of the users of the universe we analyse.
For the overall environment and for each community we can compute: average distance,
diameter, each user’s PageRank and other graph centrality measures, etc.

12. 11
Community analysis:
V&A Museum (London)
There are six different interest-based
communities among V&A followers:
design, fashion, journalism…
Some (purple, red and blue) are easier to
identify, others aren’t as there are
significant ties between users
in these communities.
The average distance shows us that most
of the connections are asymmetrical.

13. 12
Community analysis:
Palazzo Madama (Torino)
There is an international museum-related group
(yellow) and Italian communities: Torino group (red),
Italian media (green), museum and culture
(cyan blue)
The ties in this smaller community
are much stronger than in the V&A.
Palazzo Madama communicates in two languages,
which naturally splits its community into two groups.

14. 13
Community analysis:
CCCB (Barcelona)
There is a strong group (in green)
related to @ciutatmorta, the documentary
that got to be global-trending topic
and was previously exhibited at CCCB.
Most of the tweets were published in
the week after the broadcast.
News and marketing actions generate large
numbers of tweets that may hide other
more-constant but low-volume interactions.
There are ways to overcome this problem.

15. 14
Diversity to increase information spread
and avoid stagnation
Diversity within the set of our followers
is important to ensure both growth and information spread.
Stagnation in follower growth is a usual problem,
often related to lack of diversity.
The analysis shows why same number of followers split in
different geographic and topic-centred communities will lead
to
a greater growth and word spread than the same number
of followers in a single local community.

16. 15
Diversity to increase information spread
and avoid stagnation

17. 16
Community analysis:
Data Beers BCN

18. 17
Community analysis:
Data Beers BCN

19. 18
Community analysis:
Data Beers BCN

20. 19
Ø The followers of our followers are a small set within Twitter 300+ million monthly active users.
Ø We have shown that 70 to 90% of follower’s growth occurs within that set.
Ø There are tactics to maximise the size of this set.
And they are based on the analysis of the graph structure.
Ø Applying community analysis algorithms (thanks Gephi!)
we can gain actionable insights on our social media environment.
To recap…

21. www.lamagnetica.com
lamagnetica@lamagnetica.net
(+34) 93 539 09 90
Thank you
@lamagnetica