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SECOND SCREEN AND POLITICAL TALK-SHOWS:
MEASURING AND UNDERSTANDING THE ITALIAN
PARTICIPATORY «COUCH POTATO»
Fabio [.] Gig...
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 pre...
Dataset
• From 30th of August 2012 to 30th June 2013
• 11 political talk-shows
• Hashtags: #ballarò or #ballaro, #portaapo...
Definitions
•
•
•
•

Original Tweets < Tweet-(RT+Reply)
Engagement < Peaks in Original Tweets
Window < span of n minutes a...
Methods
• Peaks detection (Marcus et al 2011)
• Text-mining of Tweets created during each
window to find the top 5 frequen...
Results RQ1
AVERAGE TWEETS

AVERAGE WINDOW SPAN
(MINUTE)

VARIABLE

N

AVERAGE TWEETS-PER-MINUTE

Group discussion

135

5...
RQ2 sample
PEAK TIME

TWEETS

ORIGINAL TWEETS

SPAN (MINUTE)

Group discussion

11/10/2012 22:36

123

102

1

Interview

...
Codebook
FORM
Objectivity
Attention seeking

Inbound

CONTENT

Outbound

Pure
information

Subjectivity
Emotion

Interpret...
Codebook example
AUDIENCE PARTICIPATION
Attention-seeking

Emotion
Opinion

POLITICAL PARTICIPATION

#piazzapulita are you...
Results RQ2
PERCENT OF ALL TWEETS
(N = 2,017)

PERCENT OF TWEETS CODED

PERCENT OF TWEETS CODED

AS POLITICAL PARTICIPATIO...
Results RQ3
PERCENT OF TWEETS CODED AS

PERCENT OF TWEETS CODED AS

POLITICAL PARTICIPATION

AUDIENCE PARTICIPATION

(N=1,...
Conclusions
• Interviews is the sub-genre associated with
the highest levels of Tweet-per-minute (TPM)
• The use of Twitte...
Thanks for the attention!
• Working paper available at
http://ssrn.com/abstract=2345240
• Dataset is partially available a...
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Second Screen and Participation: a Content Analysis of a Full Season Dataset of Tweets

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

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Transcript of "Second Screen and Participation: a Content Analysis of a Full Season Dataset of Tweets"

  1. 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. 2. Outline • • • • • • Research questions Dataset Definitions Methodology Results Conclusions
  3. 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. 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. 5. 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
  6. 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. 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. 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. 9. Codebook FORM Objectivity Attention seeking Inbound CONTENT Outbound Pure information Subjectivity Emotion Interpretation Objectivised opinion Opinion originally based on Wohn, Na 2011
  10. 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. 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. 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. 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. 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
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