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Saturday, 28 November 2015

And the winner isn't....

Social media analysis got several of the big predictions wrong at last night’s Academy Awards in Hollywood. James Verrinder reports.

As the movie industry nurses its collective head in the aftermath of last night’s Oscars, those in the business of making predictions based on social media chatter will be scratching theirs and wondering why their forecasts were so wide of the mark.

Despite critics hailing The Artist as almost a dead cert to take home the Best Picture award, social media analysis in the run-up to the event suggested it might not turn out to be the one-horse race many were expecting.

At the University of Southern California’s Annenberg Innovation Lab, Professor Jonathan Taplin studied the sentiment in tweets to predict that Woody Allen’s Midnight in Paris would take the award. Meanwhile social media agency Banyan Branch’s analysis of tweets and Facebook ‘likes’ tipped The Help as the front-runner. But in the end, it was the critics who called it correct.

Banyon also used tweets and likes to pick the likely winners for the Best Actor and Best Actress categories. Again they proved incorrect, with The Help’s Viola Davis and Moneyball’s Brad Pitt failing to turn their social media popularity into an Oscar. Eventual Best Actress winner Meryl Streep came a close second in Banyon’s analysis, but it was way off the mark for Best Actor. It had The Artist’s Jean Dujardin in third place.

General Sentiment was another company playing the predictions game – and it did so better than others, thanks to its approach of analysing social media and Twitter sentiment alongside the bookies’ odds for each nominee. First let’s start with the missteps. It made The Artist’s Bérénice Bejo the favourite to take home Best Supporting Actress ahead of The Help’s hotly tipped – and ultimately successful – Octavia Spencer. It also tipped The Help’s Davis to beat Streep.

Yet General Sentiment was right on the money in the Best Picture, Best Actor and Best Supporting Actor categories – the last of which went to Christopher Plummer, as predicted, for his performance in Beginners.

All in all, it wasn’t a great night for demonstrating the predictive abilities of social media. However, it should be noted that the public has no input into which films and performers actually win the Oscars – that job is handled by the Academy Awards panel. With that in mind, this year’s presidential campaign and the upcoming mayoral election in London are the two bigger tests of social media analysis as a means of gauging the public mood.

Until then, forget your sorrows with this amusing little skit from last night’s ceremony: supposedly a long-lost recording of the first screentest and focus group for the Wizard of Oz.

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Readers' comments (13)

  • Hi,

    we actually got it right using social media data. Here's how

    As per the studies you mentioned, also referenced in our study wrap up, they both looked at sheer general mentions of a movie, which to be honest is not a very useful way of looking at social data in this case because getting an Award does not equal being socially relevant (as you get voted on by a panel of practitioners).

    Which is one of the reasons why for example we looked at mentions of a movie in relation to a specific award and also looked at external data in order to 'augment' the online audience feedback (social media) with offline behaviours (box office) and the expert views (the critics, the bookies, the practitioners)

    More coming soon on which data sources got closer to the right results...


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  • Brian Tarran

    Thanks Fran. That's a great post – and congratulations on getting the prediction right.

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  • Brian Tarran

    Just to add: I would like to see the breakdown of which data source was closest when you have it.

    Looking at your blog, it made me wonder whether the social media data really added that much. It seems you would have arrived at The Artist as your predicted winner based solely on bookies' odds and critic reviews.

    Which, I guess, begs the question: what's the point of social media predictions?

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  • We started seeing The Artist run away when we isolated social media mentions of a movie in relation to each award, before looking at any other data stream.

    Sentiment was only helpful when layered on top of Social mentions in relation to an award, not when layered on top of the movie's general mentions.

    And with bookies, while the odds was fairly consistent in the UK, in the US there were quite a few bookies pointing at The Help or Moneyball or The Descendants, so not a clear picture.

    It's only when we married social mentions in relation to the awards with critics and bookies that things started shaping clearly in favour of The Artist.

    So I guess social in this case helped providing bottom up validation or grounding to a top-down betting exercise (either by critics or the bookies)

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  • Brian Tarran

    I stand corrected. Thanks for explaining that, Fran.

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  • pleasure. to be honest when I saw those social media predictions based on sheer volumes of tweets and likes to facebook pages I couldn't really understand WHY they were doing it that way :)

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  • another proper social media mining approach, backing The Artist

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  • Brian Tarran

    I saw that article last night too. They also got Best Actress right, but Best Actor and Best Director wrong.

    A 50% hit rate then, versus General Sentiment's 3/5.

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  • that's because they didn't mash it up :) to be honest we didn't have the time to look at the other categories in our study, but we collected the data so hopefully we'll be able to review it all at some point.

    I guess with this one we wanted to see if there was a way to use social media for something that isn't inherently social, and then pilot data mashups to see what sticks... nothing more ambitious than that really

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  • Brian Tarran

    Clearly a successful first experiment, though, Fran.

    Also – just to add in case you missed it – Radian6 scored 2/3:

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