The document analyzes tweets containing the hashtag "#SpanishRevolution" from April-May 2013 during economic/housing crises in Spain. Key findings include: 1) "#SpanishRevolution" is strongly connected to hashtags like "#15M", "#StopDesahucios", representing opposition to governing parties and social/political issues at the time. 2) Related hashtags referred to important social actors ("#15M"), calls to action ("#NoLesVotes") or metaphorically represented anti-eviction movements with colors. 3) The main discourse among #SpanishRevolution tweets centered around "#15M", "#VAEO", "#Nolesvotes" and was focused/proposed surprisingly by

1. 1
The Spanish Revolution in Twitter (1): Hashtags,
Escraches and Anti – Evictions social movement in Spain
Estrella Gualda (estrella@uhu.es)
Juan D. Borrero (jdiego@uhu.es)
José Carpio (jose.carpio@dti.uhu.es)
University of Huelva
1st IMASS conference, Methods and An
in Social Sciences, 23-24 April 2014, O
Portugal, http://imass.ca/imass/confe

2. Table of contexts
ework
uraging Mobilization
ntages and changes with Micro-blogging
tional advantages of micro-blogging websites
urn to micro-discourses
of micro-discourses included in Twitter
ext and Topic of Study
and anti-evictions social movements
e Success of the Anti-Evictions Social Movements in Spain
ctives
hods
collection
ysis
lts
tative analysis (Atlas ti): Codification and analysis of micro-
urses contained in the tweets
e final codes in Atlas ti and the original terms in the tweets
Results (cont.)
Basic description of the #SpanishRevolution: Global patterns
Co-ocurrences of codes in tweets
Qualitative analysis (Atlas ti): First Exploration of co-
ocurrences of codes (#)
Codes exported to Spss. Testing of hypothesis in Spss
combination and triangulation between Qualitative and
Quantitative analysis .
Importance of hashtags in the #Spanish Revolution dataset
Network of o-occurences among #s within the
#SpanishRevolution discourse
Network of significative correlations among # linked to the
#SpanishRevolution discourse
Tweet’s Authors
Summarizing
Discussion
Conclusions & Following Steps

3. Framework
Encouraging Mobilization
• Old Revolutions and Social Movements dissemination:
• meetings, assemblies, demonstrations, and also through instruments as pamphlets,
posters, by word of mouth, and similar.
• At the end of the twentieth century the process of encouraging external
mobilization used to be supported by a combination of different media:
• TV, mailing, webpages or messages disseminated through mobiles.
• At the beginning of the XXI, the Web 2.0 based on the developing of Social
Networks through the Internet introduced new ways of announce or call
any type of protest, meeting, etc.
• Diffusion by very effective and fast means, on real-time
• Twitter, Facebook, WhatsApp and similar social media, that were added to other
traditional ones.
• Mobile devices (smart phones…) open up new ways to communicate and share
content.

4. Framework
Advantages and changes with Micro-blogging
• Micro-blogging changed some parameters of the collective
mobilization:
• Strategies for spreading the movement, the potential scope of the
dissemination, etc.
• Micro-blogging reflects the human desire to share and consume
information and knowledge (Allen et al. 2011)
• Mobile devices can directly share content such as micro-blogs without
Internet infrastructure
• Profits in scalability
• The potential to provide content relevant to the end user without explicit
subscriptions

5. Framework
Additional advantages of micro-blogging websites
As argumented by Allen et al. (2011):
• Micro-blog posts (short messages) require less time and effort to write than
‘traditional’ blog posts, yet still allow wide distribution among social networks
when compared to email or instant messaging.
• Also brevity further allows the reader to easily filter large numbers of messages.
• And even the broadcast nature of reduces the cognitive threshold for the writer
to decide to share and the burden of readers to process all updates.
• The structure of the networks induced by micro-bloggers and their followers
makes them an ideal mechanism for rapid dissemination of information amongst
ad hoc social communities.

6. Framework
The turn to micro-discourses
• Discourses: From old Philosophy to recent semantics and discourse analysis (linguistics)
and conversation analysis (that study the codified language of a field of enquiry and the
statements; relations among language and structure and agency, in different social and
human sciences).
• It refers to written and spoken communications
• Words or terms linked together that say something about: Meaning (Ferrater, 1994)
• Semiotic: Set of signs (*) with different ways of significance and used with different aims
(Ferrater, 1994:917)
• Signs: an arbitrary or conventional mark or device that stands for a word, phrase, etc; symbols; gestures, etc.
• Ogden and Richards (1923):
• Symbolic discourses (referential)
• Emotive/ expressive discourses: feelings, attitudes…
• Morris
• Informative: Give information
• Valorative: Say opinions
• Provocative: Provoke actions
• Sistemic

7. Framework
The turn to micro-discourses
• Foucault, discourse is what is said, and it is framed and connected to
a paradigm in which world is organized
• discourse describes “an entity of sequences, of signs, in that they are
enouncements”. The term discursive formation conceptually describes the
regular communications (written and spoken) that produce such discourses
• There exist internal relations within a given discourse, and external
relations among discourses
• Discourse are not isolated, but in relation to other discourses

8. Framework
Type of micro-discourses included in Twitter
• Twitter: users send and read "tweets", which are text messages
limited to 140 characters
• Hashtags: users can group posts together by topic or type by the use
of hashtags – words or phrases prefixed with a “#” sign.

9. Context and Topic of Study
Crisis and anti-evictions social movements
• Economic crisis in Spain
• Topic: “desahucios/ evictions”, an important Spanish social
problematic today that has emerged with the economic crisis and
propelled an intense ‘anti-evictions social movement’, with the drive
of the PAH, the Platform of Mortgage Victims and other supports.

10. Context and Topic of Study
Some Success of the Anti-Evictions Social Movements in Spain
• Interest of this movement
• 1112 evictions stopped by the PAH (Platform of Mortgage Victims)
• Rehousing of 1106 people by PAH’s Social Work
• International Projection
• Deeds of Assignment in Payment (Daciones en pago)
• Deliver of the house in order to clear the outstanding debt (used to solve the
problem of unpaid mortgages in Spain with the crisis time). Alternative to the
foreclosure (the bank follow the law and sell the house in a public auction to earn
the debt.
• http://www.bankimia.com/dacion-en-pago
• Increasing of organization (PAH): Empowerment, formation and auto organization
of people
• Motions in Town Halls

11. Objectives
• To analize the use of the hashtag “SpanishRevolution” in a extracted
dataset of tweets concerning ‘desahucios’.
• To describe the main other hashtags included in the tweets in which
the hashtag “SpanishRevolution” was found.
• To discover the connections between this and other hashtags
included in the same tweets, looking for patterns in the micro
discourses produced by the hashtags.
• To determine the patterns and types of hashtags included in the
tweets, that is, are the hashtags alluding to slogans, places, people, or
to what?
#SpanishRevolutio

12. Methods
Data collection
Extraction of Big Data
• In particular we did a follow-up of all the tweets published in Twitter
from 10 April 2013 to 28 May 2013. During these dates it was
extracted all tweets that contained the chains or keywords
“desahucios”, “#stopdesahucios”, and the user “@stopdesahucios”.
The data extraction produced a dataset of 499,420 tweets.
• We selected the sub-sample of tweets containing the
“#SpanishRevolution” for the analysis, in order to answer our
objectives

13. • Pluralistic methodology concerning strategies and techniques of
research
• With the help of the Qualitative Software Atlas ti, we codified and
analyzed the micro-discourses contained in the tweets, explored co-
ocurrences of codes and, finally exported the work to Spss for testing
some hypothesis under a quantitative analysis, producing a
combination and triangulation between Qualitative and Quantitative
analysis .
Methods
Analysis

14. First exploration of data (Word cruncher)
Identification of significative # within the tweets
Coding of most used # in their ‘context units’ (Automatic coding)
(see example next slide)
Manually coding solving problems of mis-spealling or similars
Automatic coding in Atlas ti under one unique code, representing a
significative category for further analysis
Examples:
12M|12m|12deMayo|12m18h|12m2013/12M2013|12Mai|12-May|may-
12
Results
Qualitative analysis (Atlas ti): Codification and analysis of micro-discourses
contained in the tweets

15. Some final codes in Atlas ti and the original terms
in the tweets
• 15M = #15M|#15m*|#15M2013|#15m2013
• ESCRACHE = #Escrach*|#ESCRACH*|#escrach*|#SCRACHES*|#scratch*|#Escrche|#escraces|#Escratches| #escratx*|
#escrche|#*escrache*|#*Escrache*|#*ESCRACHE*|#*scrach*|#*Scrach*|#*SCRACH*|#ESCRACHE
• DESAHUCIOS= #desahucios|#Desahucios|#desahucio|#desaucios
• STOP DESAHUCIOS= #StopDesahuci*|#stopdesahuci*|#stodesa*|#StopDesahicios|#Stopdeshacuios|#StopDeshaucios|#StopDeshucios
• SPANISH REVOLUTION=
#SpanishRevolution|#spanishrevolut*|#spainrevolution|#span?shrevol*|#SpanishRevolution|#SpahishRevolution|#spanishrevolutiòn
• SIN_ILP_SENADO ACABADO= #SinILPsenadoAcabado|#SinILPSenadoAcabado|#SinILPsenadoAcabado*
• NO_LES_VOTES= #NoLesVotes|#Nolevotes
• SISEPUEDE= #SíSePu*|#SiSePuede|#sisep*|#SiSePot|#SiSePuede12M|#SISEPUEDO|#SíPodem|#sípodemos
• 12M= #12M|#12m|#12deMayo|#12m*|#12M*|#12Mai|#12-May|#may-12
• 25A= #25A|#25a|# 25-abr
• ESPAÑA= #Espagne|#EsPAHña|#Espana|#espanha|#espania|#Espanol|#espanya|#España|#España
• RajoyDimisión= #RajoyVeteYa|#Rajoydimision|#RajoyDimisión|#RajoyDimisiónYa|#RajoyDimissió
• PRIMAVERA VERDE=
#Primaeraverde|#PrimaveraCaliente|#PrimaveraVede|#PrimaveraVerde|#PrimaverVerde|#primaeraverde|#primavera|primaveraverde|
#PrimaveraVerde|#PRIMAVERAVERDE
• 12M15M= #12M15M|#12m15m|#
• MAREABLANCA= #mareablanca|#MareaBlanca21AbrilUNETE|#MareaBlancaRELOADED|#mareblanca|#Mareasblancas

16. Original database: 499,420 tweets
1,354 tweets including #SpanishRevolution, 22% of them are re-tweets (RT). Only 0.2%
were modified tweets (MT).
93.8% cite a URL within the tweet
Results
Basic description of the #SpanishRevolution: Global patterns
0,00
10,00
20,00
30,00
40,00
50,00
60,00
70,00
80,00
90,00
100,00
Re-tweets/ Total Modified tweets/
Total
Cite 0 URL within
the tweet/ Total
Cite 1 URL within
the tweet/ Total
Cite 2 or more
URLs within the
tweet/ Total
Basic description of #SpanishRevolution
0 5000 10000 15000 20000 25000 30000 35000 40000
ers: Mean of followers
Users: Mean of friends
sers: Mean of statuses
2584,83
1203,15
37860,54
Data of Users

17. Co-ocurrences of codes in tweets
Different # in the
same tweet

18. Results
Qualitative analysis (Atlas ti): First Exploration of co-ocurrences of codes (#)

19. - Importance of # in the SpanishRevolution dataset
- Networks of co-ocurrences in the discourse
- Network of Significative correlations among hashtags
- Tweet’s Authors
Results
Codes exported to Spss. Testing of hypothesis in Spss combination and
triangulation between Qualitative and Quantitative analysis .

20. Importance of hashtags in the #Spanish
Revolution dataset
0 10 20 30 40 50 60 70 80 90
15M
NOLESVOTES
VAEO
SANIDAD
MAREA_VERDE
12M
12M15M
25A
PAH
SÍSEPUEDE
DESAHUCIOS
ESCRACHE
STOP DESAHUCIOS
SIN_ILP_SENADOACABADO
ESPAÑA
INDIGNADOS_INDIGNACIÓN
MAREA_BLANCA
RAJOY_DIMISIÓN
ILP
PRIMAVERA_VERDE
REVOLUCIÓN
VIVIENDA_SOCIAL
Tags (#) by Frequency of co-ocurrence with
#SpanishRevolution
MAIN DISCOURSE PRODUCED BY
HASHTAGS LINKED TO
#SPANISHREVOLUTION
15M
NOLESVOTES
VAEO
SANIDAD
MAREAVERDE
12M – 12M15M
25A
PAH
SÍSEPUEDE

21. Network of co-occurences among #s within
the #SpanishRevolution discourse
cores.
ferent colors
CIRCLE = Actor
SQUARE = Slogan
UP TRIANGLE = Mobilization
dates
BOX = Topics
DOWN TRIANGLE = Places

22. Network of significative correlations among #
linked to the #SpanishRevolution discourse
Size of Node: Degree

23. Tweet’s Authors
244 different authors in 1,354 different tweets (One and a half month of follow
up)
Basic Pattern of Authorship:
1. Big centralization
2. Long tail
0
100
200
300
400
500
600
700
800
GustavoDalmasso
RubénDrughieri
25SMurcia
democraciarealmurcia
BrunoJordán
UnMundoSinDinero
Alvarian
AteneuRoig
Cristianh.S.
EstefaníaAlfonso
JavierMtzGarrido
LazarodeTormes
Nulladiessineline
PuppetMaster
SaldaaS75
VicenteCervantes
juanlumontes
nedaangelofiran
#occupybrussels
AmyCook
Bewegung30.09.
DavidPellissoNavas
GlobalRevolution
JasminBlessed
Kamchatka#1J
MiguelHerencia
PaulaColladosM
RAKELRIVERO
RocioRebazaJara
ScrappyBadger
VocesCríticas
alfonsodiez
ernstd
Centralization
Long tail
ors (more than 10
ts of total 1354)
Frequency Percentage
avo Dalmasso 700 51.7
lutions Info 151 11.1
OE 35 2.6
o Acevedo 23 1.7
aDuende 11 0.8
Who are they?

24. Results
Summarizing
• Results suggest that the hashtag ‘SpanishRevolution’ is thematically strongly
connected to other as, for instance, ‘15M’, ‘MareaVerde’, ‘NoLesVotes’, ‘Sanidad’,
‘Vaeo’ or StopDesahucios’, all of them representing alternate discourses to that of
the governing party in Spain, or specific sociopolitical battles at the time of the
big data extraction.
• At this time these hashtags suppose mentioning different type of phenomena, as
important collective actors (‘15M’), calls for actions or slogans as ‘NoLesVotes’ or
the metaphoric ‘MareaVerde’ symbolically representing the anti-evictions
movements with the ‘SiSePuede’ in green color in the streets.
• Also, other hashtags as ‘#Escrache’ was especially connected to #12m, #12m15m,
#15m, #NoLesVotes, #SíSePuede and #Vaeo also representing important
sociopolitical dimensions at the micro discourse level.
• Main discourse of hashtags addresed around the #SpanishRevolution is focused
on ‘15M’, ‘VAEO’, ‘Nolesvotes’, a clear political turn proposed surprisenly by one
to three actors.

25. Discussion
• In fact, through this analysis we that around a particular hashtag exist
a discursive construction if we observe connections between
hashtags that have been included in the same tweets.
• Provocative discourses claming for particular actions as ‘No les votes’,
• Call for action, critics, search for global social and political changes
also symbolic included under the ‘15M’ most cited hashtags in this
dataset.

26. Conclusions & Further Research
• Emergence of non-visible connections between # and strategies
behind (implications for opinion trends creation, advertisement,
policies, etc.)
• Discoursive trends in Twitter through conglomerates of #. Few words
to generate, defend, or sell complex ideas (anti-evictions philisophy,
mobilization, etc.)
• Few actors dominate the production of “micro-discourses”, hidden
leaderships in Twitter for normal users
• Technical applications to improve

27. Thanks a lot for your attention!
Muito obrigada pela sua atenção!
• Estrella Gualda (estrella@uhu.es)
• Juan D. Borrero (jdiego@uhu.es)
• José Carpio (jose.carpio@dti.uhu.es)
University of Huelva