In a word: no.

TweetReach, a reach analysis service that I like and blogged about here, have been tracking tweets about the Academy Awards for the past month, and have crunched that data into a report that shows exactly who the Twitter collective predicts to win.

In that time, 170 thousand people have tweeted more than 313,000 times about the Oscars, reaching 53.5 million unique Twitter accounts and generating more than 720 million impressions.

So, here are our final Academy Award winner predictions, based on the cumulative unique reach of the nominees.

Best Supporting Actress: Hailee Steinfeld (True Grit)
Best Supporting Actor: Geoffrey Rush (The King’s Speech)
Best Actress: Natalie Portman (Black Swan)
Best Actor: Colin Firth (The King’s Speech)
Best Picture: Black Swan The King’s Speech

Sounds good. But here’s the problem: users on Twitter have absolutely zero influence on how the Academy Award winners are picked. This isn’t the People’s Choice Awards. Oscar winners are voted for and determined by the Academy themselves.

So, unless the 5,835 members of the Academy of Motion Picture Arts and Sciences (AMPAS) have been tweeting constantly about their exact picks, it doesn’t make any difference what Twitter thinks. It’s like asking 100 random people in the street for the winner of the Kentucky Derby, and betting accordingly. Sure, you might get lucky, but that’s all it will be – luck.

Twitter can be a great predictor of many things, especially where sentiment plays a major role in deciding the outcome of an event that is shaped by the public. For example, the winner of American Idol, or even who is most likely to be the next occupant of the White House.

What Twitter can’t do is predict the outcome of an event in which the people polled have no control or influence over whatsoever.

If Twitter’s collective does get the Oscar winners right it will simply be a coincidence. At best an educated guess. And as much as it might seem that I’m being a nitpicker extraordinaire, by any measure that’s quite a bit different to a prediction.