Communications of the ACM
Study Uses Grins and Frowns to Predict Online Game Hits
Researchers in Taiwan recorded the electrical signals of muscles involved in positive and negative emotions when subjects played a new online game, and are using that data to predict whether people would find the game addictive.
Credit: Multimedia Networking and Systems Lab/Institute of Information Science, Academia Sinica
Researchers at Academia Sinica's Institute of Information Science have developed a method for predicting an online game's success by studying gamers' initial emotional response. The researchers analyzed the movements of gamers' smile and frown muscles during the first 45 minutes of play.
The model should be able to forecast a game's addictiveness according to facial electromyography (EMG) measures from a focus group, according to the researchers. First, the researchers used archival game data and several EMG experiments for a forecasting model that could predict a game's ability to retain active players for a long time. Next, they analyzed the account activity records of 11 games, generated a general addictiveness index, and then gathered 155 hours of facial-expression data from 84 gamers.
The researchers hope to help game publishers avoid wasting money on bad investments and to proceed with developing games that are more likely to succeed.
From Phys.Org
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Abstracts Copyright © 2013 Information Inc., Bethesda, Maryland, USA
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