Presentation delivered during the 14th Annual Conference of the Association of Internet Researchers (October 23-26, 2013 in Denver, Colorado).

1. SECOND SCREEN AND POLITICAL TALK-SHOWS:
MEASURING AND UNDERSTANDING THE ITALIAN
PARTICIPATORY «COUCH POTATO»
Fabio [.] Giglietto [@uniurb.it]
Department of Communication Studies and Humanities | Università di Urbino Carlo Bo
OCTOBER 23-26, 2013 - DENVER, COLORADO

2. Outline
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Research questions
Dataset
Definitions
Methodology
Results
Conclusions

3. Research Questions
• RQ1: what is the prevalent sub-genre
broadcasted during peaks of Twitter activity?
• RQ2: what is prevalent use behind this
messages and across the different typologies
of sub-genres?
• RQ3: what is the prevalent form of
participation found in this Tweets across the
different uses and typologies of sub-genres?

4. Dataset
• From 30th of August 2012 to 30th June 2013
• 11 political talk-shows
• Hashtags: #ballarò or #ballaro, #portaaporta, #agorarai,
#ultimaparola, #serviziopubblico, #inmezzora, #infedele or
#linfedele, #ottoemezzo, #omnibus, #inonda, #piazzapulita
• Complete dataset from Twitter firehose (DiscoverText + GNIP)
• Raw n. of Tweets collected: 2,489,669 (76% onair - 187.031
unique onair contributors)
• 1,076 episodes with Twitter (tweet, rt, reply, contributors,
reach, original tweets) metrics and audience ratings
• Twitter metrics per minutes from 30 August 2012 to 30 June
2013 (n=439,204)

5. Definitions
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Original Tweets < Tweet-(RT+Reply)
Engagement < Peaks in Original Tweets
Window < span of n minutes around the peak
TV scene < excerpt of a TV program aired
during a window

6. Methods
• Peaks detection (Marcus et al 2011)
• Text-mining of Tweets created during each
window to find the top 5 frequently used term
(tf-idf) and automatic label the window
• Manual classification of windows in six typologies
of political talk-shows sub-genres broadcasted
during the corresponding scene
• Content analysis of Tweets (in the context of the
scene) created during one window for each subgenre

7. Results RQ1
AVERAGE TWEETS
AVERAGE WINDOW SPAN
(MINUTE)
VARIABLE
N
AVERAGE TWEETS-PER-MINUTE
Group discussion
135
501
3
163.9
Interview
86
1,876
3
584.6
One-on-one interview
51
768
2.6
288.6
Pre-recorded video
5
525
2.8
184.7
Satire
5
258
2.4
176.2
External intervention
4
696
5.5
194.4

8. RQ2 sample
PEAK TIME
TWEETS
ORIGINAL TWEETS
SPAN (MINUTE)
Group discussion
11/10/2012 22:36
123
102
1
Interview
04/02/2013 21:56
151
103
1
One-on-one interview
20/09/2012 21:53:03
843
598
7
Pre-recorded video
16/05/2013 21:33:02
828
523
5
Satire
05/02/2013 21:20:02
819
476
4
External intervention
21/03/2012 22:59
255
126
1
3,019
2,017

9. Codebook
FORM
Objectivity
Attention seeking
Inbound
CONTENT
Outbound
Pure
information
Subjectivity
Emotion
Interpretation
Objectivised
opinion
Opinion
originally based on Wohn, Na 2011

10. Codebook example
AUDIENCE PARTICIPATION
Attention-seeking
Emotion
Opinion
POLITICAL PARTICIPATION
#piazzapulita are you eventually going to
ask Tremonti why they forced us to budget
balance?
@pbersani do you understand the
difference between electoral-campaignpromises and project? #piazzapulita
@PiazzapulitaLA7
Laughs and sags all together while watching There is not so much to do: I adore #renzi
Crozza #ballarò
#Ballarò
#piazzapulita: a pressing and really
Good Bersani. I am appreciating him. Direct
interesting interview. This is the kind of
and concrete. #piazzapulita
journalism I like!
Crozza/Berlusconi is not so as funny as the
Objectivised opinion original… #ballarò
Schifani has been vilified by Travaglio for
five years. If he had asked for reply, they
would have cried scandal #serviziopubblico
Interpretation
Also Formigli covertly incites Polverini to
resign #piazzapulita
Unexpected lapse of style by the Senate
President #Grasso on #serviziopubblico.
Pure information
Formigli asks to Polverini the real question: “We are betting to win for our reliability. I
“Why haven’t you fight for cuts before?”
won’t do anything else” @pbersani on
#piazzapulita
#piazzapulita #ItaliaGiusta and #pb2013

11. Results RQ2
PERCENT OF ALL TWEETS
(N = 2,017)
PERCENT OF TWEETS CODED
PERCENT OF TWEETS CODED
AS POLITICAL PARTICIPATION
AS AUDIENCE PARTICIPATION
(N=1,217)
(N=800)
Attention-seeking
21***
14***
Emotion
5
5
6
Opinion
59
19
14
15*
12*
Objectivised opinion
33
30***
40***
Interpretation
12
14***
8***
Pure information
15
14**
18**
Frequency of Typologies of Tweets by Political and Audience Participation
Note: Chi-squares were calculated for Tweets coded as audience and political participation. * p < .05, ** p < .01, *** p < .001

12. Results RQ3
PERCENT OF TWEETS CODED AS
PERCENT OF TWEETS CODED AS
POLITICAL PARTICIPATION
AUDIENCE PARTICIPATION
(N=1,217)
(N=800)
Group discussion
87***
13***
Interview
83***
17***
One to one interview
87***
13***
Pre-recorded video
61***
39***
Satire
21***
79***
External intervention
29***
71***
Frequencies of Sub-Genres by Political and Audience Participation
Note: Chi-squares were calculated for Tweets coded as audience and political participation. * p < .05, ** p < .01, *** p < .001

13. Conclusions
• Interviews is the sub-genre associated with
the highest levels of Tweet-per-minute (TPM)
• The use of Twitter to express personal
opinions is the prevalent one
• Especially in political participation, proposing
a personal point of view as a fact is a
commonly used strategy
• Polarization between audience and political
participation

14. Thanks for the attention!
• Working paper available at
http://ssrn.com/abstract=2345240
• Dataset is partially available at
http://figshare.com/articles/Twitter_e_Talk_S
how_Politici_in_Italia_2012_2013_/808606
• Other materials from the project:
– Comprehensive presentation of the project
– Working paper on Audience/Tweets correlation