Second Screen and Political Talk-Shows: Measuring and Understanding the Italian Participatory...
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![Page 1: Second Screen and Political Talk-Shows: Measuring and Understanding the Italian Participatory «Couch Potato»](https://reader033.fdocuments.net/reader033/viewer/2022060110/555dbf52d8b42a68328b5c9a/html5/thumbnails/1.jpg)
Fabio [.] Giglietto [@uniurb.it]
Department of Communication Studies and Humanities | Università di Urbino Carlo Bo
SECOND SCREEN AND POLITICAL TALK-SHOWS:MEASURING AND UNDERSTANDING THE ITALIAN
PARTICIPATORY «COUCH POTATO»
SOCIAL MEDIA - THE FOURTH ANNUAL TRANSFORMING AUDIENCES CONFERENCE
WESTIMIINISTER UNIVERSITY, LONDON, 2-3 SEPTEMBER 2013
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Outline
• Dataset
• 3 research questions
– Methodology
– Data Analysis
– Results
• Conclusions
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Dataset
• From 30th of August 2012 to 30th June 2013
• 11 political talk-shows
• Hashtags: #ballarò or #ballaro, #portaaporta, #agorarai, #ultimaparola, #serviziopubblico, #inmezzora, #infedele or #linfedele, #ottoemezzo, #omnibus, #inonda, #piazzapulita
• Complete dataset from Twitter firehose (DiscoverText + GNIP)
• Raw n. of Tweets collected: 2,489,669
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Research Questions
• RQ1: is the activity on Twitter statistically correlated with TV audience?
• RQ2: what kind of TV scene corresponds to the highest engagement on Twitter?
• RQ3: Twitter commentaries on political talk-shows are at the cross-road between political and audience participation. Which of these two types of participation is the most common and how it is played?
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IS THE ACTIVITY ON TWITTER CORRELATED WITH TV AUDIENCE?
RQ1
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RQ1 dataset
• Subset of Tweets (1) created during the on air time of the episodes (+15 mins) and (2) containing the corresponding program #hashtag (n= 1,881,873)
• 1,077 aired episodes with respective average audience and rating as estimated by Auditel
• Twitter metrics for each episode (Tweets, contributors, reach, ReTweet, Reply, Tweet-per-minute, contributors-per-minute)
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Correlation coefficents
Audience n p
Tweet .54 1077 < .01
Contributors .64 1077 < .01
Reach .51 1077 < .01
ReTweet .54 1077 < .01
Reply .6 1077 < .01
Tweet-per-minute (TPM) .57 1077 < .01
Contributors-per-minute (CPM) .67 1077 < .01
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Audience ~ CPM
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Loglinear transformation
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Log(Audience) ~ Log(CPM)
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Correlations
Audience n p
Tweet .54 1077 < .01
Contributors .64 1077 < .01
Reach .51 1077 < .01
ReTweet .54 1077 < .01
Reply .6 1077 < .01
Tweet-per-minute (TPM) .57 1077 < .01
Contributors-per-minute (CPM) .67 1077 < .01
Log (CPM) .86 1077 < .01
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Results (1/2)
• Over the eight different metrics tested, the observed correlation coefficient with the audience was > 0.5
• The rate of Tweet per minute (TPM) and contributors per minute (CPM) correlate remarkably well (when log transformed respectively r=0.83 and 0.86 with p < .01)
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Results (2/2)
• A multiple regression model based on the (1) average audience of previously aired episodes, (2) CPM and (3) networked publics variable*, explained 96% of the variance in the audience
• Taking all other variables constant, we expect an increase of 0.37% in audience for an increase of 1% in average CPM
* representing the inclination of the audience base of a show to contribute to the conversation with the official hashtag while the show is on air
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WHAT KIND OF TV SCENE CORRESPONDS TO THE HIGHEST ENGAGEMENT ON TWITTER?
RQ2
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Definitions
• Period <- span of n days between main political events of the season
• Original Tweets < Tweet-(RT+Reply)
• Engagement < Peaks in Original Tweets
• Window < span of n minutes around the peak
• TV scene < excerpt of a TV program aired during a window
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Dataset
• Twitter metrics (tweet, rt, reply, contributors, reach, original tweets) by minutes from 30 August 2012 to 30 June 2013 (n=439,204)
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Daily activity overview by period
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Average dalily activity by period
Nat
ion
al e
lect
ion
s
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Activity by minute in an outlier episode
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Methods
• Peaks detection (Marcus et al 2011)
• Textmining of Tweets created during each windows to find the top 5 frequent term (td-idf) extraction and automatic labeling of the peaks
• Triangluated content analysis of a random sample of 22 peaks (2 for period): (1) Tweets created during the peak, (2) peak’s label and (3) correponding TV events (retived via web streaming)
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286 peaks detected
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Peaks detected in the outlier episode
POLVERINI, PIAZZAPULITA, GOCCE, LAZIO, RENATA POLVERINI, PIAZZAPULITA, FOTOGRAFO, LAZIO, GIUNTA
full list of peaks with automatic labels
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Preliminary results (1/2)
• During the whole season: more than half of the analyzed peaks correspond to scene with controversial guests, screams, fights, squabbles and occasional bad words
• During the first part of the season: scandals, anti-politics and the critics agaist the austerity measures decided by Monti’s government
• During the campaign: Silvio Berlusconi (whenever present as a guest, imitated by a comedian or critizied by his opponents)
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Preliminary results (2/2)
• After the elections and before the formation of the new government: any opportunity for critizing the democratic party and call for a more direct participation
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WHICH TYPES OF PARTICIPATION (AUDIENCE/POLITICAL ) IS THE MOST FREQUENT? HOW IT IS PLAYED?
RQ3
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Methods
• Triangulated content analysis of a random sample of 22 peaks (2 for period): (1) Tweets created during the window, (2) peak’s label and (3) correponding TV events (retived via web streaming)
• Dataset of all Tweets in each windows(n=7,394) excluding ReTweets
• Codeset > modified version of a code matrix originally developed by Wohn & Na, 2011
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Codeset
Attention Seeking
Emotion
Information Opinion
Attention Seeking
Emotion
Information Opinion
Audience Participation
Political Participation
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Preliminary results (1/2)
• Audience participation prevails during scene with controversial guests, screams, fights, squabbles and bad words
• Political participation prevails duringinterviews
• Both for audience and political participation, information often express opinions by hilighting agreed, disagreed or underlinewrong sentences
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Preliminary results (2/2)
• Opinions are often addressed to a general imagined audience but sometimes are directly addressed (even using the @) to a guest or to the host of the show as in an imaginary peerto peer conversation
• Emotions are often hilighted by the use of multple exclamation marks
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Limitation and future works
• RQ1 results are applicable to the analyzedgenre only (movies, tv series, etc probablyworks in a different way)
• In order to better address RQ2 all peaks should be analyzed with the same method
• RQ3 should be tested not only around peaks
• In both cases it would be good to have more than one coder in order to get intercoderreliability
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Thanks for the attention!
Working paper on RQ1 is available on SSRN
Please address comments, suggestions and remarks to @fabiogiglietto