Fabio [.] Giglietto [@uniurb.it]

Department of Communication Studies and Humanities | Università di Urbino Carlo Bo

SECOND SCREEN AND POLITICAL TALK-SHOWS:MEASURING AND UNDERSTANDING THE ITALIAN

PARTICIPATORY «COUCH POTATO»

OCTOBER 23-26, 2013 - DENVER, COLORADO

Outline

• Research questions

• Dataset

• Definitions

• Methodology

• Results

• Conclusions

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?

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)

Definitions

• 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

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 sub-genre

Results RQ1

VARIABLE N AVERAGE TWEETSAVERAGE WINDOW SPAN

(MINUTE)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

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

Codebook

FORM

Objectivity Subjectivity

CONTENT

InboundAttention seeking Emotion

Outbound

Pure information

Interpretation Objectivisedopinion

Opinion

originally based on Wohn, Na 2011

Codebook example

AUDIENCE PARTICIPATION POLITICAL PARTICIPATION

Attention-seeking#piazzapulita are you eventually going to ask Tremonti why they forced us to budget balance?

@pbersani do you understand the difference between electoral-campaign-promises and project? #piazzapulita@PiazzapulitaLA7

EmotionLaughs and sags all together while watching Crozza #ballarò

There is not so much to do: I adore #renzi #Ballarò

Opinion#piazzapulita: a pressing and really interesting interview. This is the kind of journalism I like!

Good Bersani. I am appreciating him. Direct and concrete. #piazzapulita

Objectivised opinionCrozza/Berlusconi is not so as funny as the original… #ballarò

Schifani has been vilified by Travaglio for five years. If he had asked for reply, they would have cried scandal #serviziopubblico

InterpretationAlso Formigli covertly incites Polverini to resign #piazzapulita

Unexpected lapse of style by the Senate President #Grasso on #serviziopubblico.

Pure informationFormigli asks to Polverini the real question: “Why haven’t you fight for cuts before?” #piazzapulita

“We are betting to win for our reliability. I won’t do anything else” @pbersani on #piazzapulita #ItaliaGiusta and #pb2013

Results RQ2

PERCENT OF ALL TWEETS

(N = 2,017)PERCENT OF TWEETS CODED

AS POLITICAL PARTICIPATION

(N=1,217)

PERCENT OF TWEETS CODED

AS AUDIENCE PARTICIPATION

(N=800)

Attention-seeking 19 21*** 14***

Emotion 5 5 6

Opinion 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

59

Results RQ3

PERCENT OF TWEETS CODED AS

POLITICAL PARTICIPATION

(N=1,217)

PERCENT OF TWEETS CODED AS

AUDIENCE PARTICIPATION

(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

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

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_Show_Politici_in_Italia_2012_2013_/808606

• Other materials from the project:

– Comprehensive presentation of the project

– Working paper on Audience/Tweets correlation