Lecture about data and election.

1. how to analyse
the politics using
massive/small
social media data
the #GeneralElection in the
UK on Twitter
@fabiomalini
Visiting Scholar
King’s College London

2. My overview
- to present th concept of perpetual
beta and its implications to extract and
to analyse data.
- how to analyse small data from
#GeneralElection on Twitter.
- how to analyse massive data from
#GeneralElection on Twitter.

3. The idea of
"perpetual beta"
and its
implications to
political
communication.

4. Tim O'Reilly (2010) published his
manifesto suggesting new ways
for the digital economy.
This text showed a set of changes
would be faced by companies like
Amazon, Google, Facebook,
Microsoft.
This text named these changes as
Web 2.0.
It’s term was entirely created
within the technology industry.

5. I would like to
draw attention
to the notion
of perpetual
beta from this
manifesto.

6. What
does
"perpetual beta"
mean?

7. Perpetual is
happening all
the time.
Beta means
that a product
is being tested.

8. Basically the
software would be
substituted by the
idea of platform or
application.
In 2010, It was
ending the notion
that enable to claim
that every software
hadshould be a
complete version to
be used.
It was the end of the
software release
cycle.

9. From this time every software
will become an online service.
It starts, but it never ends.
This is the materialization of
app culture, where all is
"perpetual beta".
All have permanently been
modified, updated, corrected,
getting feedback from users,
adjusting it to improve the
platform.

10. Why points up this
"suggestion" made
by Tim O'Reilly?

11. This reason for this is to
explore the fact that social
actors and movement
have become perpetual
beta, not just softwares.
People (called profile)
every day have a new
hashtag to engage
themselves.

12. Movements need to
aggregate new campaigns.
Politicians must launch a
new agenda or opine about
a new headline.
How come? Because when
we become an account on
social media, we must
update.
We are a perpetual
beta data.

13. This huge online activity
from social media users
is producing a massive
data about different
topics discussed by
many individuals,
institutions, groups,
collectives etc.

14. It's possible to identify
patterns in small data
and in massive data
extracted from social
media platforms
(Twitter, Facebook,
Instagram, Youtube,
etc).

15. Patterns in
small data

16. I've collected the last
3216 (maximum
allowed by Twitter)
from Jeremy Corbyn
and Boris Johnson's
account on Twitter.
Every user with public
content allows that the
their content can be
extracted and analyzed.

17. After generating my
dataset (tweets.csv),
I’ve parsed it, and I’ve
got around 50 files to
help my analyse.

18. I was looking for 3
patterns:
- preferential
connectivity: with which
actors there is a big
number of ties.
- framing: what the
profile emphasizes in their
discourse.
- media environment:
what sources of
information the profile puts
more trust.

19. @borisjohnson
Conservative Party
Last 2887 tweets. 23%, RTs (1235).
First tweet: 01/04/2015
Frequency: 1,69 tweets/day
top word: “brexit”.

20. Preferential
connectivity
- share more official sources:
@10downingstreet,
@foreignoffice,
conservatives.
- it's more self-centered:
share his own account
(@borisjohson and
@backboris).
- Telegraph is his main source
of information to spread trust
among his followers.

21. Media enviroment:
- traditional journalism:
share trust and legitimize his
actions.
- links from the Tory platform:
focused in producing own
narrative.

22. framing:
what topic is emphasized.
- support the Brexit deal.
- NHS, security, and school
associated with his priorities.

23. @jeremycorbyn
Labour Party
Last 3216 tweets. 40%, RTs (1235).
First tweet: 25/09/2018
Frequency: 7.39 tweets/day
top word: NHS

24. Preferential connectivity
* Jonathan Ashworth: a politician
who is an expert in health care.
* his party (Labour).
* John Mcdonne: MP.
* Angela Rayner: a politician who
is an expert in education care.

25. media environment:
- traditional journalism:
share trust and legitimize
their actions.
- links from social media
platform: share links.
There is huge importance
given to journalism. The
value is to get credibility
from the media, showing
that he has support from
some papers.
It's curious because many
far-right politicians refuse
this support, setting up
their own online media
environment.

26. framing:
what discourse is
emphasized.
- against cuts and taxs (austerity).
- criticize brexit deal.
- define NHS as the main cause.

27. People are their conections
on the social media
plataform.
We are “other” people many times
on social media platform.
We are a network stance.
#GeneralElection no Twitter

28. Pattern in
massive data
This graph - or network - is made up
by 232k users posting messages with
#GeneralElection, #GE19.
The dataset is made up by 830k
tweets from 20/11 up to 1/12/19.
User is a point.
Retweet is a link (line).
Each color represents a community
(users who retweet each other
generating those many authors called
bubble).
I used a program called Gephi to do
it.

29. Pattern in
massive data
The image shows 4 groups of users
that are spreading their perspective
about the #GeneralElection.
It's hard to know that each
represents, so a solution is to know
the usernames.
---- First pattern: the stance, the
position, the perspective.
---- Others: Preferential connectivity,
media environment, and framing.

30. Analyse in
detail
Follow me on Twitter:
@fabiomalini

31. Conclusion
To work with massive or small social
media data, firsty you have to ask.
You need to track and to map
relations.
After that, you have to reveal the
different perspectives in conflict
regarding just one will win.