1.SECOND SCREEN AND POLITICAL TALK-SHOWS:MEASURING AND UNDERSTANDING THE ITALIANPARTICIPATORY COUCH POTATOFabio [.] Giglietto [@uniurb.it]Department of Communication Studies and Humanities | Universit di Urbino Carlo BoCOMPOSITE NARRATIVES POLITICS AND (SOCIAL) MEDIA PARTECIPATION 14 MARCH 2013 UNIVERSIT 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 Twitterconversations about a TV genre (talk-show) Why political talk-show? 5. Participatory couch potatoand Networked PublicsParticipation Networked PowerPublicsas a noun (audience)as an adjective (public matters, public space) 6. Second Screen Today Used device while watching TV 86 88 66US smartphone ownersUS tablet ownersUS 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. Second Screen Todayused 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 werebreaks is something theywere 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. 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 thewalking dead, 10,000 a minute for x-factor Superbowl gathered 24,000,000 tweets thisyear compared to 14,000,000 last year, UEFAchampions 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. Research Objectives Measuring the Italian participatory couchpotato (favorite show/episodes, level ofparticipation) Developing a technique aimed at detectingkey moments (during the season and withinepisodes) for later discourse/content analysis Developing a statistical model aimed atpredicting the audience of an episode fromTwitter activity 10. Dataset * From 30th of August 2012 to 10th March 2013 11 political talk-shows Hashtags: #ballar or#ballaro, #portaaporta, #agorarai, #ultimaparola, #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 timeof the episodes (+15 mins) 607 aired episodes, with respective averageaudience and rating as estimated by Auditel Total n. of Tweets in the subset: 1,126,787 * at the time of writing 12. Exploratory Data Analysisshow episodes total_tweet avg_audience avg_tweet avg_tweet_magorarai109 58835 586764.9 539.77 3.97Ballar242112104295958.3 8800.41 53.33In mezzora 1444841294642.8 320.28 7.11inonda 48 85495 846229.1 1781.14 12.81Linfedele 146022 813877.6 430.14 2.53omnibus 169 15114 242600.589.43 0.68ottoemezzo1191227631760786.2 1031.62 16.09piazzapulita 161458221458878.3 9113.87 53.08portaaporta57 816231647087.7 1431.98 11.01ServizioPubblico 163329303242717.7 20808.12 122.76ultimaparola 21 62489 855285.7 2975.66 24.14 13. Exploratory Data Analysis 14. Exploratory Data Analysis 15. Exploratory Data Analysis 16. Exploratory Data Analysis 17. Exploratory Data Analysis r=0.805r=0.863 18. Cluster Analysis kmeans(centers=10, nstart=100) 19. A closer look atthe high activity cluster 20. Statistical ModelingTPM AVG_AUDIENCE AVG_AUDIENCE+ TPM+ TPM + NETWORKED PUBLICSResidual standard error 0.44020.2269 0.2173Multiple R-squared0.76710.9381 0.9434p-value:< 0.001 < 0.001< 0.001 21. Statistical Modelingshow episodes total_tweet avg_audience avg_tweet avg_tweet_m networked_publicsagorarai 109 58835 586764.9539.77 3.970.00091991Ballar 24 211210 4295958.3 8800.41 53.330.002048534In mezzora1444841294642.8320.28 7.11 0.000247393inonda48 85495 846229.1 1781.14 12.810.002104803Linfedele146022 813877.6430.14 2.530.00052851omnibus169 15114 242600.5 89.43 0.68 0.000368639ottoemezzo 119 122763 1760786.2 1031.62 16.090.000585887piazzapulita16 145822 1458878.3 9113.87 53.080.006247179portaaporta 57 816231647087.7 1431.98 11.010.000869403ServizioPubblico16 332930 3242717.720808.12122.760.006416878ultimaparola21 62489 855285.7 2975.66 24.140.003479149 22. Statistical ModelingINTERCEPT AVG_AUDIENCE TPMNETWORKED PUBLICS1.41838 0.816240.15128-0.11692airdate TPM ESTIMATED AUDIENCEPREDICTIONAUDIENCEERRORPiazza Pulita 2-12-2013 63.53 1,506,946 1,170,000 -336,946Ballar 3-12-2013 54.07 4,043,652 4,280,000 236,348Agor 3-12-2013 2.7 557,148 633,000 75,852Otto e Mezzo3-12-2013 30.62,067,906 1,649,000 -418,906 23. Conclusions A note on data gathering with Twitter It seems that each talk-shows develop apeculiar relationship with their onlineaudience (Piazza Pulita) Clustering appear to be a promising way todiscover key episodes in a seasons The campaign made results more interestingbut also more difficult to predict 24. Conclusions Audience and Tweet-per-minute aresignificantly correlated A model based on TPM only seems to beunable to efficiently predict the episodeaudience Metrics extrapolated form Twitter activitycould be successfully used to increase theprecision of the prediction based on averagepast audience 25. To-do Extrapolating more Twitter metrics form thedataset (RT, Reply) Visualizing, clustering and using these metrics (ora combination of) as predictors Digging into a more detailed analysis of oneprogram along the season or specific keyepisodes Defining a ladder of participatory couchpotato Access, Interaction, Participation(Read, RT, Reply, Original Tweets that influencethe program schedule and topics 26. To-doAccess Interaction Participation ReadReTweet Reply Original Tweet