Predicting motion picture box office performance using temporal tweet patterns

Purpose The purpose of this paper is to investigate temporal tweet patterns and their effectiveness in predicting the financial performance of a movie. Specifically, how tweet patterns are formed prior to and after a movie’s release and their usefulness in predicting a movie’s success is explored. Design/methodology/approach Volume was measured and sentiment analysis was performed on a sample of Tweets posted four days before and after the release of 86 movies. The temporal pattern of tweeting for financially successful movies was compared with those that were financial disappointments. Using temporal tweet patterns, a number of machine learning models were developed and their predictive performance was compared. Findings Results show that the temporal patterns of tweet volume, length and sentiment differ between “hits” and “busts” in the days surrounding their releases. Compared with “busts” the tweet pattern for “hits” reveal higher volume, shorter length, and more favourable sentiment. Discriminant patterns in social media features occur days in advance of a movie’s release and can be used to develop models for predicting a movie’s success. Originality/value Analysis of temporal tweet patterns and their usefulness in predicting box office returns is the main contribution of this research. Results of this research could lead to development of analytical tools allowing motion picture studios to accurately predict and possibly influence the opening night box-office receipts prior to the release of the movie. Also, the specific temporal tweet patterns presented by this work may be applied to problems in other areas of research.