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»
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
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 PublicsParticipation 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 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
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
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)
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
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-doAccess Interaction Participation Read ReTweet Reply Original Tweet
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