Early Prediction of Movie Box Office Success based on Wikipedia Activity Big Data

Use of socially generated "big data" to access information about collective states of the minds in human societies has become a new paradigm in the emerging field of computational social science. A natural application of this would be the prediction of the society's reaction to a new product in the sense of popularity and adoption rate. However, bridging the gap between "real time monitoring" and "early predicting" remains a big challenge. Here we report on an endeavor to build a minimalistic predictive model for the financial success of movies based on collective activity data of online users. We show that the popularity of a movie can be predicted much before its release by measuring and analyzing the activity level of editors and viewers of the corresponding entry to the movie in Wikipedia, the well-known online encyclopedia.Comment: 13 pages, Including Supporting Information, 7 Figures, Download the dataset from: http://wwm.phy.bme.hu/SupplementaryDataS1.zi

Can electoral popularity be predicted using socially generated big data?

Today, our more-than-ever digital lives leave significant footprints in cyberspace. Large scale collections of these socially generated footprints, often known as big data, could help us to re-investigate different aspects of our social collective behaviour in a quantitative framework. In this contribution we discuss one such possibility: the monitoring and predicting of popularity dynamics of candidates and parties through the analysis of socially generated data on the web during electoral campaigns. Such data offer considerable possibility for improving our awareness of popularity dynamics. However they also suffer from significant drawbacks in terms of representativeness and generalisability. In this paper we discuss potential ways around such problems, suggesting the nature of different political systems and contexts might lend differing levels of predictive power to certain types of data source. We offer an initial exploratory test of these ideas, focussing on two data streams, Wikipedia page views and Google search queries. On the basis of this data, we present popularity dynamics from real case examples of recent elections in three different countries.Comment: To appear in Information Technolog

Wisdom of the Crowd Vs Reviews of the Experts: A Case Study Regarding Predicting Movie Box-Office Results

Teadlased on aastakümneid tegelenud filmide kassatulu ennustamisega, sest iga aasta linastub suur hulk teoseid, mille tulemused üllatavad nende rahastajaid kas heal või halval viisil, sõltuvalt esialgsetest prognoosidest. Eelnevad uurimustööd on avaldanud vastakaid tulemusi filmikriitikute arvustuste kasutamise kohta filmide kassatulu ennustamiseks. Niisamuti on kaasatud sotsiaalmeedia ühe võimaliku andmeallikana filmide müügiedu prognoosimiseks. Käesolevas töös uuritakse, milline neist kahest erinäolisest allikast on kasulikum ennustamaks parema täpsusega filmide kasumlikkust. Uuritavateks andmeteks oleme kogunud viimase kolme aasta jooksul linastunud Hollywoodi ja Bollywoodi filmid, mis on erineva geograafilise asukoha ning kultuurilise taustaga. Kollektiivse tarkuse näitena uurime sotsiaalvõrgustiku Twitteri andmeid ning võrdleme neid filmikriitikute arvustustega Hollywoodi ning Bollywoodi filmiportaalidest Metacritic ja SahiNahi. Kaasame mitmeid erinevaid tunnuseid ning rakendame erinevaid masinõppe algoritme ennustusmudelite ehitamiseks. Meie vaatluste tulemused näitavad, et võrreldes filmikriitikute eksperthinnangutega pole kollektiivsete teadmiste abil võimalik filmide kassatulu paremini ennustada ega vastupidi.Predicting movie sales figures has been a topic of interest for research for decades since every year there are dozens of movies which surprise investors either in a good or bad way depending on how well the film performs at the box-office compared to the initial expectations. There have been past studies reporting mixed results on using movie critics reviews as one of the sources of information for predicting the movie box-office outcomes. Similarly using social media as a predictor of movie success has been a popular research topic. In this thesis, we perform a case study to evaluate out of two – the (wisdom of the) crowd or the movie critics reviews, which one can predict the outcome of the movies more accurately. We analyze the Hollywood and Bollywood movies from the last three years, which belong to two different geo as well as cultural locations. We used Twitter for collecting the wisdom of the crowd and used movie critics review scores from movie review aggregator sites Metacritic and SahiNahi for Hollywood and Bollywood movies respectively. To perform our evaluation, we extracted various features and used them to build prediction models using different machine learning algorithms. After measuring the performance of prediction models using features from both Twitter and movie critic reviews, we did not find conclusive evidence to declare a clear-cut winner

Movie Industry Economics: How Data Analytics Can Help Predict Movies’ Financial Success

Purpose: Data analytics techniques can help to predict movie success, as measured by box office sales or Oscar awards. Revenue prediction of a movie before its theatrical release is also an important indicator for attracting investors. While measures for predicting the success of a movie in box office sales and awards are widely missing, this study uses data analytics techniques to present a new measure for prediction of movies’ financial success.Methodology: Data were collected by web-scraping and text mining. Classification and Regression Tree (CART), Random Forests, Conditional Forests, and Gradient Boosting were used and a model for prediction of movies' financial success proposed. Content strategy and generating high profile reviews with complex themes can add to controversy and increase the chance of nomination for major movie awards, including Oscars.Findings/Contribution: Findings show that data analytics is key to predicting the success of movies. Although predicting sales based on data available before the release remains a difficult endeavor, even with state-of-the-art analytics technologies, it potentially reduces the risk of investors, studios and other stakeholders to select successful film candidates and have them chosen before the production process starts. The contribution of this study is to develop a model for predicting box office sales and the chance of nomination for winning Oscars. Practical Implications: Cinema managers and investors can use the proposed model as a guide for predicting movies’ financial success