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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




                                                              COMPOSITE NARRATIVES POLITICS
                                             AND (SOCIAL) MEDIA PARTECIPATION 14 MARCH 2013
                                                           UNIVERSITÀ DEGLI STUDI DI BERGAMO
Summary
•   TV has always been social, BUT…
•   Participatory “couch potato” & Networked Publics
•   Second screen today
•   Research objectives
•   Dataset
•   Data analysis
    – Exploratory
    – Cluster analysis
    – Statistical modeling
• Conclusions
TV has always been social, BUT…




             http://www.youtube.com/watch?v=xEZ2W5-l1Zo
TV has always been social, BUT…
• The 4 properties make it different
• First study on a full season dataset of Twitter
  conversations about a TV genre (talk-show)
• Why political talk-show?
Participatory «couch potato»
          and Networked Publics

Participation                           Networked

   Power                                    Publics



                as a noun (audience)

                  as an adjective
           (public matters, public space)
Second Screen Today
                                                     Used device while watching TV

                             86                                                             88


                                                                                                                                                           66




      US smartphone owners                                                  US tablet owners                                              US laptop owners
Sources:
Google. (2012). The New Multi-screen World: Understanding Cross-platform Consumer Behavior. Mountain View, CA. Retrieved from
http://www.thinkwithgoogle.com/insights/library/studies/the-new-multi-screen-world-study/
Nielsen. (2012). Double Vision – Global Trends in Tablet and Smartphone Use while Watching TV | Nielsen Wire. nielsen-wire. Retrieved October 16, 2012, from
http://blog.nielsen.com/nielsenwire/?p=31338
Second Screen Today
                                                                  used their phones to
                             38




                                                                                           11                                                             11




   keep themselves occupied see what others were saying post their own comment
     during commercials or online about a program they about a program they were
    breaks is something they      were watching                 watching
         were watching
Source:
Smith, A., & Boyles, J. L. (2012). The Rise of the “Connected Viewer”. Washington. Retrieved from http://pewinternet.org/Reports/2012/Connected-viewers.aspx
Second Screen Today
• 400mm Tweet per day
• 200mm monthly active users on Twitter
• 1 in 3 tweets about TV
• +12,000 tweets a minute (TPM) for “the
  walking dead”, 10,000 a minute for “x-factor”
• Superbowl gathered 24,000,000 tweets this
  year compared to 14,000,000 last year, UEFA
  champions league 110,000 tweets a minute
Source:
Jane Deering Davis, How Twitter Has Changed How We Watch TV, SXSW Panel (https://soundcloud.com/officialsxsw/how-twitter-has-changed-how-we)
Research Objectives
• Measuring the Italian participatory «couch
  potato» (favorite show/episodes, level of
  “participation”)
• Developing a technique aimed at detecting
  key moments (during the season and within
  episodes) for later discourse/content analysis
• Developing a statistical model aimed at
  predicting the audience of an episode from
  Twitter activity
Dataset   *




• From 30th of August 2012 to 10th March 2013
• 11 political talk-shows
• Hashtags: #ballarò or
  #ballaro, #portaaporta, #agorarai, #ultimaparo
  la, #serviziopubblico, #inmezzora, #infedele or
  #linfedele, #ottoemezzo, #omnibus, #inonda,
  #piazzapulita
• Raw n. of Tweets collected: 1,703,064

                                   * at the time of writing
Dataset    *




• Subset of Tweet created during the airing time
  of the episodes (+15 mins)
• 607 aired episodes, with respective average
  audience and rating as estimated by Auditel
• Total n. of Tweets in the subset: 1,126,787




                                   * at the time of writing
Exploratory Data Analysis

show             episodes total_tweet avg_audience avg_tweet avg_tweet_m
agorarai              109       58835     586764.9     539.77         3.97
Ballarò                24      211210    4295958.3 8800.41           53.33
In mezzora             14        4484    1294642.8     320.28         7.11
inonda                 48       85495     846229.1 1781.14           12.81
L'infedele             14        6022     813877.6     430.14         2.53
omnibus               169       15114     242600.5      89.43         0.68
ottoemezzo            119      122763    1760786.2 1031.62           16.09
piazzapulita           16      145822    1458878.3 9113.87           53.08
portaaporta            57       81623    1647087.7 1431.98           11.01
ServizioPubblico       16      332930    3242717.7 20808.12         122.76
ultimaparola           21       62489     855285.7 2975.66           24.14
Exploratory Data Analysis
Exploratory Data Analysis
Exploratory Data Analysis
Exploratory Data Analysis
Exploratory Data Analysis




 r=0.805            r=0.863
Cluster Analysis




                   kmeans(centers=10, nstart=100)
A closer look at
the high activity cluster
Statistical Modeling

                          TPM       AVG_AUDIENCE   AVG_AUDIENCE
                                    + TPM          + TPM
                                                   + NETWORKED PUBLICS

Residual standard error   0.4402    0.2269         0.2173

Multiple R-squared        0.7671    0.9381         0.9434

p-value:                  < 0.001   < 0.001        < 0.001
Statistical Modeling

show             episodes total_tweet avg_audience avg_tweet avg_tweet_m networked_publics
agorarai               109       58835     586764.9      539.77       3.97      0.00091991
Ballarò                 24     211210     4295958.3     8800.41     53.33      0.002048534
In mezzora              14        4484    1294642.8      320.28       7.11     0.000247393
inonda                  48       85495     846229.1     1781.14     12.81      0.002104803
L'infedele              14        6022     813877.6      430.14       2.53      0.00052851
omnibus                169       15114     242600.5       89.43       0.68     0.000368639
ottoemezzo             119     122763     1760786.2     1031.62     16.09      0.000585887
piazzapulita            16     145822     1458878.3     9113.87     53.08      0.006247179
portaaporta             57       81623    1647087.7     1431.98     11.01      0.000869403
ServizioPubblico        16     332930     3242717.7    20808.12    122.76      0.006416878
ultimaparola            21       62489     855285.7     2975.66     24.14      0.003479149
Statistical Modeling
          INTERCEPT           AVG_AUDIENCE   TPM        NETWORKED PUBLICS

          1.41838             0.81624        0.15128    -0.11692




                    airdate       TPM         ESTIMATED     AUDIENCE    PREDICTION
                                              AUDIENCE                  ERROR
Piazza Pulita       2-12-2013     63.53       1,506,946     1,170,000   -336,946
Ballarò             3-12-2013     54.07       4,043,652     4,280,000   236,348
Agorà               3-12-2013     2.7         557,148       633,000     75,852
Otto e Mezzo        3-12-2013     30.6        2,067,906     1,649,000   -418,906
Conclusions
• A note on data gathering with Twitter
• It seems that each talk-shows develop a
  peculiar relationship with their online
  audience (Piazza Pulita)
• Clustering appear to be a promising way to
  discover key episodes in a seasons
• The campaign made results more interesting
  but also more difficult to predict
Conclusions
• Audience and Tweet-per-minute are
  significantly correlated
• A model based on TPM only seems to be
  unable to efficiently predict the episode
  audience
• Metrics extrapolated form Twitter activity
  could be successfully used to increase the
  precision of the prediction based on average
  past audience
To-do
• Extrapolating more Twitter metrics form the
  dataset (RT, Reply)
• Visualizing, clustering and using these metrics (or
  a combination of) as predictors
• Digging into a more detailed analysis of one
  program along the season or specific key
  episodes
• Defining a “ladder” of participatory “couch
  potato” Access, Interaction, Participation
  (Read, RT, Reply, Original Tweets that influence
  the program schedule and topics
To-do



Access             Interaction       Participation


 Read    ReTweet     Reply       Original Tweet

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Second Screen and Political Talk-Shows: Measuring and Understanding the Italian Participatory «Couch Potato»

  • 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 COMPOSITE NARRATIVES POLITICS AND (SOCIAL) MEDIA PARTECIPATION 14 MARCH 2013 UNIVERSITÀ DEGLI STUDI DI BERGAMO
  • 2. Summary • TV has always been social, BUT… • Participatory “couch potato” & Networked Publics • Second screen today • Research objectives • Dataset • Data analysis – Exploratory – Cluster analysis – Statistical modeling • Conclusions
  • 3. TV has always been social, BUT… http://www.youtube.com/watch?v=xEZ2W5-l1Zo
  • 4. TV has always been social, BUT… • The 4 properties make it different • First study on a full season dataset of Twitter conversations about a TV genre (talk-show) • Why political talk-show?
  • 5. Participatory «couch potato» and Networked Publics Participation Networked Power Publics as a noun (audience) as an adjective (public matters, public space)
  • 6. Second Screen Today Used device while watching TV 86 88 66 US smartphone owners US tablet owners US laptop owners Sources: Google. (2012). The New Multi-screen World: Understanding Cross-platform Consumer Behavior. Mountain View, CA. Retrieved from http://www.thinkwithgoogle.com/insights/library/studies/the-new-multi-screen-world-study/ Nielsen. (2012). Double Vision – Global Trends in Tablet and Smartphone Use while Watching TV | Nielsen Wire. nielsen-wire. Retrieved October 16, 2012, from http://blog.nielsen.com/nielsenwire/?p=31338
  • 7. Second Screen Today used their phones to 38 11 11 keep themselves occupied see what others were saying post their own comment during commercials or online about a program they about a program they were breaks is something they were watching watching were watching Source: Smith, A., & Boyles, J. L. (2012). The Rise of the “Connected Viewer”. Washington. Retrieved from http://pewinternet.org/Reports/2012/Connected-viewers.aspx
  • 8. Second Screen Today • 400mm Tweet per day • 200mm monthly active users on Twitter • 1 in 3 tweets about TV • +12,000 tweets a minute (TPM) for “the walking dead”, 10,000 a minute for “x-factor” • Superbowl gathered 24,000,000 tweets this year compared to 14,000,000 last year, UEFA champions league 110,000 tweets a minute Source: Jane Deering Davis, How Twitter Has Changed How We Watch TV, SXSW Panel (https://soundcloud.com/officialsxsw/how-twitter-has-changed-how-we)
  • 9. Research Objectives • Measuring the Italian participatory «couch potato» (favorite show/episodes, level of “participation”) • Developing a technique aimed at detecting key moments (during the season and within episodes) for later discourse/content analysis • Developing a statistical model aimed at predicting the audience of an episode from Twitter activity
  • 10. Dataset * • From 30th of August 2012 to 10th March 2013 • 11 political talk-shows • Hashtags: #ballarò or #ballaro, #portaaporta, #agorarai, #ultimaparo la, #serviziopubblico, #inmezzora, #infedele or #linfedele, #ottoemezzo, #omnibus, #inonda, #piazzapulita • Raw n. of Tweets collected: 1,703,064 * at the time of writing
  • 11. Dataset * • Subset of Tweet created during the airing time of the episodes (+15 mins) • 607 aired episodes, with respective average audience and rating as estimated by Auditel • Total n. of Tweets in the subset: 1,126,787 * at the time of writing
  • 12. Exploratory Data Analysis show episodes total_tweet avg_audience avg_tweet avg_tweet_m agorarai 109 58835 586764.9 539.77 3.97 Ballarò 24 211210 4295958.3 8800.41 53.33 In mezzora 14 4484 1294642.8 320.28 7.11 inonda 48 85495 846229.1 1781.14 12.81 L'infedele 14 6022 813877.6 430.14 2.53 omnibus 169 15114 242600.5 89.43 0.68 ottoemezzo 119 122763 1760786.2 1031.62 16.09 piazzapulita 16 145822 1458878.3 9113.87 53.08 portaaporta 57 81623 1647087.7 1431.98 11.01 ServizioPubblico 16 332930 3242717.7 20808.12 122.76 ultimaparola 21 62489 855285.7 2975.66 24.14
  • 17. Exploratory Data Analysis r=0.805 r=0.863
  • 18. Cluster Analysis kmeans(centers=10, nstart=100)
  • 19. A closer look at the high activity cluster
  • 20. Statistical Modeling TPM AVG_AUDIENCE AVG_AUDIENCE + TPM + TPM + NETWORKED PUBLICS Residual standard error 0.4402 0.2269 0.2173 Multiple R-squared 0.7671 0.9381 0.9434 p-value: < 0.001 < 0.001 < 0.001
  • 21. Statistical Modeling show episodes total_tweet avg_audience avg_tweet avg_tweet_m networked_publics agorarai 109 58835 586764.9 539.77 3.97 0.00091991 Ballarò 24 211210 4295958.3 8800.41 53.33 0.002048534 In mezzora 14 4484 1294642.8 320.28 7.11 0.000247393 inonda 48 85495 846229.1 1781.14 12.81 0.002104803 L'infedele 14 6022 813877.6 430.14 2.53 0.00052851 omnibus 169 15114 242600.5 89.43 0.68 0.000368639 ottoemezzo 119 122763 1760786.2 1031.62 16.09 0.000585887 piazzapulita 16 145822 1458878.3 9113.87 53.08 0.006247179 portaaporta 57 81623 1647087.7 1431.98 11.01 0.000869403 ServizioPubblico 16 332930 3242717.7 20808.12 122.76 0.006416878 ultimaparola 21 62489 855285.7 2975.66 24.14 0.003479149
  • 22. Statistical Modeling INTERCEPT AVG_AUDIENCE TPM NETWORKED PUBLICS 1.41838 0.81624 0.15128 -0.11692 airdate TPM ESTIMATED AUDIENCE PREDICTION AUDIENCE ERROR Piazza Pulita 2-12-2013 63.53 1,506,946 1,170,000 -336,946 Ballarò 3-12-2013 54.07 4,043,652 4,280,000 236,348 Agorà 3-12-2013 2.7 557,148 633,000 75,852 Otto e Mezzo 3-12-2013 30.6 2,067,906 1,649,000 -418,906
  • 23. Conclusions • A note on data gathering with Twitter • It seems that each talk-shows develop a peculiar relationship with their online audience (Piazza Pulita) • Clustering appear to be a promising way to discover key episodes in a seasons • The campaign made results more interesting but also more difficult to predict
  • 24. Conclusions • Audience and Tweet-per-minute are significantly correlated • A model based on TPM only seems to be unable to efficiently predict the episode audience • Metrics extrapolated form Twitter activity could be successfully used to increase the precision of the prediction based on average past audience
  • 25. To-do • Extrapolating more Twitter metrics form the dataset (RT, Reply) • Visualizing, clustering and using these metrics (or a combination of) as predictors • Digging into a more detailed analysis of one program along the season or specific key episodes • Defining a “ladder” of participatory “couch potato” Access, Interaction, Participation (Read, RT, Reply, Original Tweets that influence the program schedule and topics
  • 26. To-do Access Interaction Participation Read ReTweet Reply Original Tweet

Editor's Notes

  1. But, at the same time, TV has never been as social as it is today
  2. General elections, Networked Publics
  3. What kind of power are we talking about?
  4. shows$networked_publics = shows$avg_tweet/shows$avg_audience
  5. shows$networked_publics = shows$avg_tweet/shows$avg_audience
  6. Although Twitter users represents only a fraction of the Italian population watching television, the average rate of Tweet-per-minutes created around the show’s hashtags during the airtime appear to be remarkably correlated with the audience of the episode as estimated by Auditel.