Paper at ECIR 2012 — Predicting IMDB Move Ratings Using Social Media

Recipient of the Best Paper Award

Andrei Oghina, Mathias Breuss, Manos Tsagkias, and Maarten de Rijke

University of Amsterdam
29 April 2012
Keywords: conference, paper, information retrieval, predictive analytics, best paper award

Abstract

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.

Part of the Artificial Intelligence Masters programme in which I used to teach was one month hands-on project. I have supervised Andrei Oghina and Mathias Breuss on whether can we predict a movie’s IMDB rating from social media before the movie is out. Andrei and Mathias collected tweets about movies and their respective YouTube trailers, and they extracted several features which were used for training a rating classifier. We have found out that we are able predict a movie’s IMDB rating with ±0.25 accuracy. That is if a movie gets an average IMDB rating of 8, we would predict 7.75 or 8.25. This is quite impressive for predicting a movie’s success before it is even released.

Our work has been published at ECIR 2012 [1], and it has been awarded the best paper award; PDF (495KB).

References

[1]: Andrei Oghina, Mathias Breuss, Manos Tsagkias, and Maarten de Rijke. Predicting IMDB Move Ratings Using Social Media. In European Conference on Information Retrieval (ECIR) 2012. ACM Library; PDF (495KB).