The nominees for the 83rd Academy Awards were announced earlier this week. The winners will be chosen on February 27. In the meantime, we thought it would be interesting to use TweetReach data to predict who might win.
There are a number of data points we could use to predict the winners. For this initial experiment, we’re going to look at four metrics: reach, exposure, tweets, and contributors. We’ll start with a baseline in today’s post and check in on the numbers every Friday until awards weekend. Then we’ll conduct a more thorough analysis and see how we did after the Oscars are handed out.
Reach. Reach is the number of unique Twitter streams that have had tweets about a particular topic delivered to them. Our Oscars reach hypothesis: The movie/actor that has reached the most unique people on Twitter will win the award.
Tweet Volume. The simplest predictive metric is overall tweet volume. Our hypothesis: The movie/actor that is tweeted about the most will win the Oscar.
Contributors. The number of unique contributors could tell us something about a movie’s chances for success at the Oscars. Hypothesis: The movie/actor with the most different people tweeting about it will win the Oscar.
Reach:Exposure. The ratio of reach to exposure gives us an idea of how diverse the Twitter audience for a topis is; higher R:E ratios indicate a wider and more diverse group of people received tweets about a topic. Hypothesis: The movie/actor with the highest R:E ratio will win the Oscar.
The Nominees Are…
We’ll look at the big awards, since they’ll generate the most Twitter traffic and give us the most data to analyze. This year’s nominees are:
And The Winners Are…
Interpretation and Other Thoughts
In the first week, the frontrunners are The King’s Speech and Inception for Best Picture, Colin Firth and Jesse Eisenberg for Best Actor, and Natalie Portman and Michelle Williams for Best Actress. We also wouldn’t discount True Grit in the Best Picture category, or James Franco and Annette Bening in the Best Actor and Actress categories.
One thing we notice immediately is that some of these queries are pretty noisy. For example, James Franco gets tweeted about a lot and many of those tweets aren’t specifically about the Academy Awards or his performance in 127 Hours. But for others, almost all the tweets about them are related to the Oscars or the movie. Jennifer Lawrence is a good example of this – nearly all the tweets about Jennifer relate to Winter’s Bone or the Academy Awards.
Basically, some of the nominees are so famous that it’s difficult to sort through general tweets about them to find only the ones related to the awards. Some of the numbers above reflect this, particularly when it comes to tweet volume. This applies to James Franco, Natalie Portman, and Nicole Kidman – they were in top spots for several metrics that relate directly to popularity. As we get closer to February 27, we anticipate that a higher percentage of tweets about the actors and actresses will be related to the Oscars, which will help with the noise. In addition, we’ll work on filtering out more non-relevant tweets.
The four metrics used in this post (reach, reach:exposure, tweet volume, and number of unique contributors) are just our first step in predicting this year’s Oscar winners. Next week, we’ll get into a more sophisticated analysis and see what else we can learn from how Twitter is talking about the Academy Award nominees.