Fabio [.] Giglietto [@uniurb.it]
Department of Communication Studies and Humanities | Università di Urbino Carlo Bo
SECOND...
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, #portaapo...
Research Questions
• RQ1: is the activity on Twitter statistically
correlated with TV audience?
• RQ2: what kind of TV sce...
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 corres...
Correlation coefficents
Audience n p
Tweet .54 1077 < .01
Contributors .64 1077 < .01
Reach .51 1077 < .01
ReTweet .54 107...
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
Rep...
Results (1/2)
• Over the eight different metrics tested, the
observed correlation coefficient with the
audience was > 0.5
...
Results (2/2)
• A multiple regression model based on the (1)
average audience of previously aired episodes,
(2) CPM and (3...
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)
• ...
Dataset
• Twitter metrics (tweet, rt, reply, contributors,
reach, original tweets) by minutes from 30
August 2012 to 30 Ju...
Daily activity overview by period
Average dalily activity by period
Nationalelections
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 frequen...
286 peaks detected
Peaks detected in the outlier episode
POLVERINI, PIAZZAPULITA, GOCCE, LAZIO, RENATA POLVERINI, PIAZZAPULITA, FOTOGRAFO, LA...
Preliminary results (1/2)
• During the whole season: more than half of the
analyzed peaks correspond to scene with
controv...
Preliminary results (2/2)
• After the elections and before the formation
of the new government: any opportunity for
critiz...
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 windo...
Codeset
Attention
Seeking
Emotion
Information Opinion
Attention
Seeking
Emotion
Information Opinion
Audience
Participation...
Preliminary results (1/2)
• Audience participation prevails during scene
with controversial guests, screams, fights,
squab...
Preliminary results (2/2)
• Opinions are often addressed to a general
imagined audience but sometimes are directly
address...
Limitation and future works
• RQ1 results are applicable to the analyzed
genre only (movies, tv series, etc probably
works...
Thanks for the attention!
 Working paper on RQ1 is available on SSRN
 Please address comments, suggestions and
remarks t...
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Second Screen and Political Talk-Shows: Measuring and Understanding the Italian Participatory «Couch Potato»

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First study on a complete dataset of Tweet

Speech presented during the 4th edition of Transforming Audiences Conference, University of Westminister - 3 Septermber 2013

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  • What I will present today is, at the best of knowledge, the first study on an entire TV season of Tweets related to a genre (political talk shows)
  • But, at the same time, TV has never been as social as it is today
  • Second Screen and Political Talk-Shows: Measuring and Understanding the Italian Participatory «Couch Potato»

    1. 1. 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
    2. 2. Outline • Dataset • 3 research questions – Methodology – Data Analysis – Results • Conclusions
    3. 3. 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
    4. 4. 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?
    5. 5. IS THE ACTIVITY ON TWITTER CORRELATED WITH TV AUDIENCE? RQ1
    6. 6. 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)
    7. 7. 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
    8. 8. Audience ~ CPM
    9. 9. Loglinear transformation
    10. 10. Log(Audience) ~ Log(CPM)
    11. 11. 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
    12. 12. 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)
    13. 13. 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
    14. 14. WHAT KIND OF TV SCENE CORRESPONDS TO THE HIGHEST ENGAGEMENT ON TWITTER? RQ2
    15. 15. 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
    16. 16. Dataset • Twitter metrics (tweet, rt, reply, contributors, reach, original tweets) by minutes from 30 August 2012 to 30 June 2013 (n=439,204)
    17. 17. Daily activity overview by period
    18. 18. Average dalily activity by period Nationalelections
    19. 19. Activity by minute in an outlier episode
    20. 20. 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)
    21. 21. 286 peaks detected
    22. 22. Peaks detected in the outlier episode POLVERINI, PIAZZAPULITA, GOCCE, LAZIO, RENATA POLVERINI, PIAZZAPULITA, FOTOGRAFO, LAZIO, GIUNTA full list of peaks with automatic labels
    23. 23. 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)
    24. 24. 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
    25. 25. WHICH TYPES OF PARTICIPATION (AUDIENCE/POLITICAL ) IS THE MOST FREQUENT? HOW IT IS PLAYED? RQ3
    26. 26. 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
    27. 27. Codeset Attention Seeking Emotion Information Opinion Attention Seeking Emotion Information Opinion Audience Participation Political Participation
    28. 28. Preliminary results (1/2) • Audience participation prevails during scene with controversial guests, screams, fights, squabbles and bad words • Political participation prevails during interviews • Both for audience and political participation, information often express opinions by hilighting agreed, disagreed or underline wrong sentences
    29. 29. 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 peer to peer conversation • Emotions are often hilighted by the use of multple exclamation marks
    30. 30. Limitation and future works • RQ1 results are applicable to the analyzed genre only (movies, tv series, etc probably works 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 intercoder reliability
    31. 31. Thanks for the attention!  Working paper on RQ1 is available on SSRN  Please address comments, suggestions and remarks to @fabiogiglietto

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