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

Outline

• Dataset

• 3 research questions

– Methodology

– Data Analysis

– Results

• Conclusions

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

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?

IS THE ACTIVITY ON TWITTER CORRELATED WITH TV AUDIENCE?

RQ1

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)

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

Audience ~ CPM

Loglinear transformation

Log(Audience) ~ Log(CPM)

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

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)

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

WHAT KIND OF TV SCENE CORRESPONDS TO THE HIGHEST ENGAGEMENT ON TWITTER?

RQ2

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

Dataset

• Twitter metrics (tweet, rt, reply, contributors, reach, original tweets) by minutes from 30 August 2012 to 30 June 2013 (n=439,204)

Daily activity overview by period

Average dalily activity by period

Nat

ion

al e

lect

ion

s

Activity by minute in an outlier episode

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)

286 peaks detected

Peaks detected in the outlier episode

POLVERINI, PIAZZAPULITA, GOCCE, LAZIO, RENATA POLVERINI, PIAZZAPULITA, FOTOGRAFO, LAZIO, GIUNTA

full list of peaks with automatic labels

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)

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

WHICH TYPES OF PARTICIPATION (AUDIENCE/POLITICAL ) IS THE MOST FREQUENT? HOW IT IS PLAYED?

RQ3

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

Codeset

Attention Seeking

Emotion

Information Opinion

Attention Seeking

Emotion

Information Opinion

Audience Participation

Political Participation

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

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

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

Thanks for the attention!

Working paper on RQ1 is available on SSRN

Please address comments, suggestions and remarks to @fabiogiglietto