This document summarizes research analyzing Twitter data related to political talk shows in Italy. The researcher collected over 1 million tweets about 11 different talk shows over a 6-month period. Exploratory analysis showed correlations between the number of tweets about an episode and its television audience. Cluster analysis identified high-activity episodes. Statistical modeling found that a model incorporating the number of tweets, average past audience, and a "networked publics" metric could predict 94% of the variation in audience sizes. The researcher concludes Twitter data has potential for predicting television audiences but more metrics should be analyzed.

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

13. Exploratory Data Analysis

14. Exploratory Data Analysis

15. Exploratory Data Analysis

16. Exploratory Data Analysis

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