Predicting IMDb Movie Ratings Using Social Media

UPDATE Apr 5, 2012: Our poster paper won the Best Poster Award at ECIR 2012.

It’s great news that our poster paper “Predicting IMDB Movie Ratings Using Social Media” with Andrei Oghina, Mathias Breuss, Manos Tsagkias, and Maarten de Rijke has been accepted in ECIR 2012, in Barcelona, Spain, 1–5 April 2012.

We predict IMDb movie ratings and consider two sets of features: surface and textual features. For the latter, we assume that no social media signal is isolated and use data from multiple channels that are linked to a particular movie, such as tweets from Twitter and comments from YouTube. We extract textual features from each channel to use in our prediction model and we explore whether data from either of these channels can help to extract a better set of textual feature for prediction. Our best performing model is able to rate movies very close to the observed values.

I found exciting that using content textual features from Twitter, and the ratio of likes over dislikes on YouTube movie trailers led to very good prediction estimates with 0.35 mean average error.