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

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Presented during "COMPOSITE NARRATIVES POLITICS AND (SOCIAL) MEDIA PARTECIPATION" – 14th March 2013 – UNIVERSITÀ DEGLI STUDI DI BERGAMO

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  • But, at the same time, TV has never been as social as it is today
  • General elections, Networked Publics
  • What kind of power are we talking about?
  • shows$networked_publics = shows$avg_tweet/shows$avg_audience
  • shows$networked_publics = shows$avg_tweet/shows$avg_audience
  • 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.
  • Second Screen and Political Talk-Shows: Measuring and Understanding the Italian Participatory «Couch Potato»

    1. 1. SECOND SCREEN AND POLITICAL TALK-SHOWS:MEASURING AND UNDERSTANDING THE ITALIANPARTICIPATORY «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. 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. 3. TV has always been social, BUT… http://www.youtube.com/watch?v=xEZ2W5-l1Zo
    4. 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. 5. Participatory «couch potato» and Networked PublicsParticipation Networked Power Publics as a noun (audience) as an adjective (public matters, public space)
    6. 6. Second Screen Today Used device while watching TV 86 88 66 US smartphone owners US tablet owners US laptop ownersSources:Google. (2012). The New Multi-screen World: Understanding Cross-platform Consumer Behavior. Mountain View, CA. Retrieved fromhttp://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, fromhttp://blog.nielsen.com/nielsenwire/?p=31338
    7. 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 watchingSource:Smith, A., & Boyles, J. L. (2012). The Rise of the “Connected Viewer”. Washington. Retrieved from http://pewinternet.org/Reports/2012/Connected-viewers.aspx
    8. 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 minuteSource:Jane Deering Davis, How Twitter Has Changed How We Watch TV, SXSW Panel (https://soundcloud.com/officialsxsw/how-twitter-has-changed-how-we)
    9. 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. 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. 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. 12. Exploratory Data Analysisshow episodes total_tweet avg_audience avg_tweet avg_tweet_magorarai 109 58835 586764.9 539.77 3.97Ballarò 24 211210 4295958.3 8800.41 53.33In mezzora 14 4484 1294642.8 320.28 7.11inonda 48 85495 846229.1 1781.14 12.81Linfedele 14 6022 813877.6 430.14 2.53omnibus 169 15114 242600.5 89.43 0.68ottoemezzo 119 122763 1760786.2 1031.62 16.09piazzapulita 16 145822 1458878.3 9113.87 53.08portaaporta 57 81623 1647087.7 1431.98 11.01ServizioPubblico 16 332930 3242717.7 20808.12 122.76ultimaparola 21 62489 855285.7 2975.66 24.14
    13. 13. Exploratory Data Analysis
    14. 14. Exploratory Data Analysis
    15. 15. Exploratory Data Analysis
    16. 16. Exploratory Data Analysis
    17. 17. Exploratory Data Analysis r=0.805 r=0.863
    18. 18. Cluster Analysis kmeans(centers=10, nstart=100)
    19. 19. A closer look atthe high activity cluster
    20. 20. Statistical Modeling TPM AVG_AUDIENCE AVG_AUDIENCE + TPM + TPM + NETWORKED PUBLICSResidual standard error 0.4402 0.2269 0.2173Multiple R-squared 0.7671 0.9381 0.9434p-value: < 0.001 < 0.001 < 0.001
    21. 21. Statistical Modelingshow episodes total_tweet avg_audience avg_tweet avg_tweet_m networked_publicsagorarai 109 58835 586764.9 539.77 3.97 0.00091991Ballarò 24 211210 4295958.3 8800.41 53.33 0.002048534In mezzora 14 4484 1294642.8 320.28 7.11 0.000247393inonda 48 85495 846229.1 1781.14 12.81 0.002104803Linfedele 14 6022 813877.6 430.14 2.53 0.00052851omnibus 169 15114 242600.5 89.43 0.68 0.000368639ottoemezzo 119 122763 1760786.2 1031.62 16.09 0.000585887piazzapulita 16 145822 1458878.3 9113.87 53.08 0.006247179portaaporta 57 81623 1647087.7 1431.98 11.01 0.000869403ServizioPubblico 16 332930 3242717.7 20808.12 122.76 0.006416878ultimaparola 21 62489 855285.7 2975.66 24.14 0.003479149
    22. 22. Statistical Modeling INTERCEPT AVG_AUDIENCE TPM NETWORKED PUBLICS 1.41838 0.81624 0.15128 -0.11692 airdate TPM ESTIMATED AUDIENCE PREDICTION AUDIENCE ERRORPiazza Pulita 2-12-2013 63.53 1,506,946 1,170,000 -336,946Ballarò 3-12-2013 54.07 4,043,652 4,280,000 236,348Agorà 3-12-2013 2.7 557,148 633,000 75,852Otto e Mezzo 3-12-2013 30.6 2,067,906 1,649,000 -418,906
    23. 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. 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. 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. 26. To-doAccess Interaction Participation Read ReTweet Reply Original Tweet

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