Everyone’s Watching It: The Role of Hype in Television Engagement through Social Media

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Presentation by Darryl Woodford & Katie Prowd, Queensland University of Technology, at the Social Media and The Transformation of Public Space Conference: Amsterdam, The Netherlands; 18 June 2014.

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  • Very broad and quick overview of a number of things we’ve been doing re: social media audiences over the past 12 months or so. {Run through list}. Some of the other projects discussed in the abstract, Aus Twittersphere / YouTube / Accession are presentations in themselves, this will concentrate on TV. I’ll mention upfront that - as we’ll discuss - some of this has gone in a commercial direction and so there are some aspects of the methods that are still being reviewed by patent attorneys
  • OffSpring spoilers: http://www.tvtonight.com.au/2013/08/offspring-why-we-killed-off-patrick.html
  • OffSpring spoilers: http://www.tvtonight.com.au/2013/08/offspring-why-we-killed-off-patrick.html
  • OffSpring spoilers: http://www.tvtonight.com.au/2013/08/offspring-why-we-killed-off-patrick.html
  • OffSpring spoilers: http://www.tvtonight.com.au/2013/08/offspring-why-we-killed-off-patrick.html
  • - Early research looked at Big Brother 15 in the US
    - Found a lot, including two keys points
    - the first- the way audiences engage, “a public performance of access”
    - tweets received while housemate, Aaryn, was in the house and tweets received after she was evicted.
  • - Early research looked at Big Brother 15 in the US
    - Found a lot, including two keys points
    - the first- the way audiences engage, “a public performance of access”
    - tweets received while housemate, Aaryn, was in the house and tweets received after she was evicted.
  • So briefly onto what we can say about methodology; my background is in the gambling industry, and thus our work has largely been based on sabermetric principles. The book on the left took a similar approach to what we have done, in this case applying sabermetrics to wall street trading firms.. And on the right, detailed implementation of a lot of methods, which have expanded my statistical knowledge.. also R plugs in to Tableau.
  • No time to go into them in detail
  • - Seasonal models of US sitcoms and dramas more or less what you would expect
    - While the patterns are obvious, we can now quantify and apply this model
    - They are essential in understanding where shows are always going to succeed, and where to expect drops.
  • - The original application, and technology, was for a web browser or web widget, however there are multiple other possible applications- iOS app for end user
    - red, orange, yellow - considers Twitter and Facebook
    - can be sorted by day, Facebook/Twitter, etc.

    - Anticipation for the show
  • While I mentioned that Facebook and Twitter share are 50/50, the picture is actually much more nuanced for individual shows.

    Some of them you would expect more than others
  • - Sunrise is very dominant in Facebook, presumably because of the interaction they request from viewers on this platform
    - The Project actually encourages people to tweet, yet there is more Facebook information
  • - The original application, and technology, was for a web browser or web widget, however there are multiple other possible applications- iOS app for end user
    - red, orange, yellow - considers Twitter and Facebook
    - can be sorted by day, Facebook/Twitter, etc.

    - Anticipation for the show
  • Essentially the goal is to go from basic metrics such as the numbers Nielsen give to more advanced forms.. The analogies I use here are from baseball. ERA - Earned Run Average - has long been the way pitchers were measured, in reports, books, tv etc, but sabermetric analysis has shown this wasn’t the best, over time the metric for pitchers have become more advanced to account for other factors under the pitchers control (XERA) then to account for the fielders behind them (FIP). In our work, doing similar for Twitter/TV; removing the effect of factors outside of show/producers control, network, day month etc. We also make use of other concepts, of which replacement value is one example.. For scheduling purposes, every show has a minimum audience based on network, and also perhaps a minimum loyal audience based on show… Very little gets 0.0, barring the Houston/LA game a few weeks ago, which Nielsen literally recorded zero people watching in the greater Houston Area (context: Astros are bad)
  • Everyone’s Watching It: The Role of Hype in Television Engagement through Social Media

    1. 1. Everyone’s Watching It The Role of Hype in Television Engagement through Social Media Darryl Woodford & Katie Prowd Queensland University of Technology Social Media and The Transformation of Public Space Amsterdam, The Netherlands 18 June 2014
    2. 2. INTRODUCTION • The Importance of Hype • Social Media Audiences • From Sabermetric to Telemetrics • The HypometerTM • Analytics work in social media still in the early stages; worth looking to other fields for more advanced methods • Why are we still concentrating on the ‘event’?
    3. 3. HYPE IN THE LITERATURE • “At four weeks out, trailer search volume on Google coupled with both the franchise status of the movie and seasonality can predict opening weekend box office revenue with 94% accuracy.” (“Quantifying Movie Magic with Google Search”, 2012) • “[S]ome reality programmes are engaged in the intentional production of a perfume of scandal and controversy” (Biltereyst, 2004 , p. 123). Similar pattern with UK, Australian & US Reality shows (c.f. Woodford & Prowd, 2014) • “[E]x ante analysis can produce reasonable estimates that allow studios to decide whether to commit additional financing and make appropriate adjustments to marketing promotions to try and induce greater gross upon release” (Kaplan, 2012, p. 1)
    4. 4. THE IMPORTANCE OF HYPE • Traditional ratings measure what people have watched, but have limited impact on what people *will* watch. • That is, they are divorced from the “decision moment”. • Yet, companies spend millions promoting shows and attempting to influence viewer behaviour, both through TV ads and through social media. • How do we measure that?
    5. 5. SOCIAL MEDIA RATINGS • Contemporary commercial social media ratings have the same limitation, even if you trust their numbers Cable Channel vs Major Network? Why is having more followers the important statistic? 2.5 hour special 1 hour show
    6. 6. HYPE ALSO ALLOWS PREDICTIONS • Hype is a significant input to a prediction algorithm for social media conversation. • By including hype, and applying other weightings to raw social media statistics, it’s possible to significantly improve predictions vs. a rolling average of previous shows. • Yet, this information still appears to be widely ignored.
    7. 7. BUT YOU NEED TO GET IT RIGHT Beamly Screenshot: 31 May 2014
    8. 8. BUT YOU NEED TO GET IT RIGHT Beamly Screenshot: 31 May 2014
    9. 9. SABERMETRICS
    10. 10. BASIC VS ADVANCED METRICS • From ERA to xERA: Includes factors under pitchers control (hits, walks etc) other than Earned Runs.. • From xERA to FIP: Accounts for the shortcoming of fielders beyond a scorers adjudication of errors. • Weighted Tweet Index: Accounts for factors unrelated to the show itself: network, day, month, etc.. • Replacement Value: Just as a ‘free agent’ in baseball has some minimal level of performance, anything shown at 8pm on CBS or Nine will get *some* viewers.. Ratings shouldn’t start at 0 (unless it’s a Houston Astros game) • Essentially, we shouldn’t just take the raw numbers; more sophisticated measures are necessary => Hype Score, Excitement Index..
    11. 11. SEASONAL MODELS • Blue Line represents the ratio of total viewers, Orange Line represents ratio of tweets (to season average per show). • In both one run seasons (top) and those with mid- season break (bottom), tweets are highly exaggerated version of traditional ratings model. • In other words: Users tweet much more around premieres & finales than regular shows. Metrics must account for this.
    12. 12. THE HYPOMETERTM • iOS app developed as functional prototype to act as a ‘modern TV Guide’ for Australian television • Calculates ‘hype’ via an algorithm which accounts for national and industry context • Ongoing evaluation of both hype figures and predictions vs. post-show TV ratings and social media engagement. • Clear trend towards ‘dynamic’ audiences; a proportion of the population on whom broadcasters should focus.
    13. 13. 32.6% 67.4% 27.7% 72.3% 10.0% 90.0% PILOT STUDY RESULTS
    14. 14. 91.5% 8.5% 62.4% 37.6% 56.7% 43.3% PILOT STUDY RESULTS
    15. 15. SOME EARLY DATA Underpromoted Overperforming High performingUnderperforming
    16. 16. INDUSTRY RESISTANCE • Across entertainment industries in general, appears to be a resistance to ‘Big Data’ - e.g. HalfBrick (Banks & Woodford, 2013) • Whether game designers, network executives or Hollywood decision makers, ‘artistic vision’ seems dominant when deciding which game to make, which pilot to pick up, which movie to dedicate millions to marketing. • Clear parallels to sabermetric movement in US sports -- Moneyball etc. • “Facts that challenge basic assumptions—and thereby threaten people's livelihood and self-esteem—are simply not absorbed. The mind does not digest them. This is particularly true of statistical studies of performance, which provide base-rate information that people generally ignore when it clashes with their personal impressions from experience.” (Kahneman, 2011)
    17. 17. Questions?

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