Analyzing #serviziopubblico: networked publics, appointment based television
and the structure of Twitter conversation
Fabio Giglietto – Università di Urbino Carlo Bo
Luca Rossi – Università di Urbino Carlo Bo
IV STS Italiana National Conference
Rovigo 21-23 June 2012
#serviziopubblico:
Political TV show aired from Nov. 3rd
2011 to June 7th 2012 (27 episodes)
Multiplatform: Satellite TV + Local TV +
Streaming
(partially) Crowdfunded: 100k
subscribers rised more than 1M €
#serviziopubblico:
12.03
10.42 9.7
8.08 8.01
4.99
6.89
7.75
8.88
7.65
6.85
6.11 5.93
6.71 6
5.2
6.3
7.66
6.2 6.2
7.08 7.02 7.5
6.26
0
2
4
6
8
10
12
14
Share (%)
research questions:
RQ1. Is the Twitter conversation network of Servizio Pubblico changing
over the several weeks of the show airing time? Is it possible to identify a
definite set of participants or are they changing every week?
RQ2. Is the conversation mainly made of comments on what is happening
on the TV show or the topic raised by the TV show are able to ignite some
debate?
RQ3. Can the Twitter activity be considered as a good indicator of a TV
show success? How can it be compared with more traditional data such
as the number of viewer or the audience
share?
dataset:
Twitter data acquired through
discovertext from Oct. 26th 2011 to
June 1st 2012.
158.240 tweets
31599 users
Subdataset: airing time of 25 episodes
of the show (9.00 pm – 00.30 am)
0
2000
4000
6000
8000
10000
12000
20
11
-Oct
-26
20
11
-Oct
-31
20
11
-No
v-0
5
20
11
-No
v-1
0
20
11
-No
v-1
5
20
11
-No
v-2
0
20
11
-No
v-2
5
20
11
-No
v-3
0
20
11
-Dec
-05
20
11
-Dec
-10
20
11
-Dec
-18
20
11
-Dec
-23
20
11
-Dec
-28
20
12
-Jan
-02
20
12
-Jan
-07
20
12
-Jan
-12
20
12
-Jan
-17
20
12
-Jan
-22
20
12
-Jan
-27
20
12
-Feb
-01
20
12
-Feb
-06
20
12
-Feb
-11
20
12
-Feb
-16
20
12
-Feb
-21
20
12
-Feb
-26
20
12
-Mar
-02
20
12
-Mar
-07
20
12
-Mar
-23
20
12
-Mar
-28
20
12
-Ap
r-0
2
20
12
-Ap
r-0
7
20
12
-Ap
r-1
2
20
12
-Ap
r-1
7
20
12
-Ap
r-2
2
20
12
-Ap
r-2
7
20
12
-May
-02
20
12
-May
-07
20
12
-May
-12
20
12
-May
-17
20
12
-May
-22
20
12
-May
-27
20
12
-Ju
n-0
1
Twitter activity
RQ1. Is the Twitter conversation network of Servizio
Pubblico changing over the several weeks of the
show airing time? Is it possible to identify a definite set of participants or are they changing every week?
Users’ activity (whole dataset)
1% of the users made
28% of the tweets while
90% of the users made 36% of the tweets
1%
9%
90%
28%
36%
36%
users tweet
Core group: (> 50% of episodes) 177 users (0,78% of the users)
The average user twitted during almost 2 episodes (1,88) [var 4,15 σ 2,03]
Core group activity:
tweet ReTweet RT/Tweet n
Core Group
22178 (19%)
4139 (14%)
18,6% 177
Main Group
92902 25233 27,1% 21996
@net (177 core users)
RQ2. Is the conversation mainly made of comments
on what is happening on the TV show or the topic
raised by the TV show are able to ignite some debate?
0
1000
2000
3000
4000
5000
6000
0 1000 2000 3000 4000 5000 6000
RT
Tweet originali
Qualitative analysis:
Title: Celentano c’è
Date: 23 Febbraio 2012
Audience: 1.688.000
Share: 6,71 %
Tweet (21:00 – 00:30): 8604 Coded Tweet: 8604
Tweeting about TV: Sharing television viewing experiences via social media message streams by D. Yvette Wohn and Eun–Kyung Na. First Monday, Volume 16, Number 3 - 7 March 2011
Coding Matrix:
Inbound Outbound
subjective Emotion Opinion
objective Att. seeking Information
*ReTweets and @reply have not been coded
Attention Seeking: «Mi sono perso qualche
informazione e/o riflessione indispensabile stasera?
Sempre gli stessi ospiti no? #serviziopubblico » [timoteocarpita – 00:15]
Emotion: «Celentano io ti amo #serviziopubblico»
[dashingdesiree – 22:45]
«Confermo: Celentano è un coglione
#serviziopubblico»
[Geras0ne – 22:44]
Information: « “La RAI sembra una succursale del
Vaticano” Marco Travaglio #serviziopubblico » [beaimpera – 23:35]
Opinion: «#serviziopubblico Sono d’accordo con
Belpietro, cosa sto fumando ? » [memolabile – 22:28]
Coded Tweet created during the show airtime (23/02/2012 21:00-00:30)
0
5
10
15
20
25
30
35
40
45
20
12-0
2-2
3 2
1:0
0
20
12-0
2-2
3 2
1:0
4
20
12-0
2-2
3 2
1:0
8
20
12-0
2-2
3 2
1:1
2
20
12-0
2-2
3 2
1:1
6
20
12-0
2-2
3 2
1:2
0
20
12-0
2-2
3 2
1:2
4
20
12-0
2-2
3 2
1:2
8
20
12-0
2-2
3 2
1:3
2
20
12-0
2-2
3 2
1:3
6
20
12-0
2-2
3 2
1:4
0
20
12-0
2-2
3 2
1:4
4
20
12-0
2-2
3 2
1:4
8
20
12-0
2-2
3 2
1:5
2
20
12-0
2-2
3 2
1:5
6
20
12-0
2-2
3 2
2:0
0
20
12-0
2-2
3 2
2:0
4
20
12-0
2-2
3 2
2:0
8
20
12-0
2-2
3 2
2:1
2
20
12-0
2-2
3 2
2:1
6
20
12-0
2-2
3 2
2:2
0
20
12-0
2-2
3 2
2:2
4
20
12-0
2-2
3 2
2:2
8
20
12-0
2-2
3 2
2:3
2
20
12-0
2-2
3 2
2:3
6
20
12-0
2-2
3 2
2:4
0
20
12-0
2-2
3 2
2:4
4
20
12-0
2-2
3 2
2:4
8
20
12-0
2-2
3 2
2:5
2
20
12-0
2-2
3 2
2:5
6
20
12-0
2-2
3 2
3:0
0
20
12-0
2-2
3 2
3:0
4
20
12-0
2-2
3 2
3:0
8
20
12-0
2-2
3 2
3:1
2
20
12-0
2-2
3 2
3:1
6
20
12-0
2-2
3 2
3:2
0
20
12-0
2-2
3 2
3:2
4
20
12-0
2-2
3 2
3:2
8
20
12-0
2-2
3 2
3:3
2
20
12-0
2-2
3 2
3:3
6
20
12-0
2-2
3 2
3:4
0
20
12-0
2-2
3 2
3:4
4
20
12-0
2-2
3 2
3:4
8
20
12-0
2-2
3 2
3:5
2
20
12-0
2-2
3 2
3:5
6
20
12-0
2-2
4 0
0:0
0
20
12-0
2-2
4 0
0:0
4
20
12-0
2-2
4 0
0:0
8
20
12-0
2-2
4 0
0:1
2
20
12-0
2-2
4 0
0:1
6
20
12-0
2-2
4 0
0:2
0
20
12-0
2-2
4 0
0:2
6
Attention Seeking Emotion Information Opinion
So
nd
ag
gio
we
b
Min
eo
Be
lpie
tro
An
nu
nzia
ta
Be
lpie
tro
Is
ola
se
r p
ub
b
Cele
nta
no
Dari
o F
o
Fra
nca
Ram
e
Be
lpie
tro
A
nn
un
zia
ta
An
nu
nzia
ta / fa
rfa
llin
a
Di P
ietr
o
RQ3. Can the Twitter activity be considered as a
good indicator of a TV show success? How can it be
compared with more traditional data such as the
number of viewer or the audience
share?
Tweet (21:00 – 00:30)
Tweet Originali
RT Url Tweet/RT
viewers - 0,15 - 0,06 - 0,25 - 0,30 0,58
Viewers Tweet activity correlation
• There is a relativly small and extremly
active part of the audience following the
show on Twitter on a regular basis;
• The topics discussed by Twitter users are
strictly related to the contents of the
show;
• Is not possible to find a significative
correlation between the users activity on
Twitter and the audience of the show;
Conclusions:
Future works:
- Comparative analysis with types of
TV shows and other italian political
shows
- Developing an improved codebook
for Twitter TV viewing
- Evaluating more complex audience
previsional models
Acknowledgements
Thanks to Servizio Pubblico staff for
provinding both the detailed share and
structure of the whole season episodes;
Thanks to Mario Orefice for the help in
coding the data.
