Researchers from ITMO University managed to predict people's personality features such as gender using data from online gaming platform. This is one of the first studies where machine learning is applied to analyze a large amount of game data. Such an approach can improve the system of personal games recommendations. It can also be used to identify gaming addiction. The results were presented at the AAAI Conference on Artificial Intelligence.
Video games are firmly established in modern life and the number of online and offline products for different platforms is growing day by day. In turn, their users every day generate more and more data that can be used to develop models of game behavior or to determine players personal characteristics. This is useful, for example, for early detection of gaming addiction, as well as for marketing research in the gaming field.
Until now, the majority of gaming research was done manually on small datasets. However, in order to make statistically significant conclusions, it is necessary to analyze large data arrays. Scientists from ITMO University and National University of Singapore are now among the first using machine learning for this. Using the data they collected about the Steam gaming platform users behavior and a specially developed and trained model, scientists managed to predict the player's gender by the game behavior.
The database for analysis was collected from the service Player.me, which provides information about both Steam and social media accounts of a person. Comparing game data with one's Twitter, Facebook and Instagram, researchers discovered some links between game behavior and personal features. As a result, the model was built upon such features as time spent on the game, achievements, preferred game genres, in-game payments, etc.
"The idea of ??my research is using game data to study human behavior in real life. Social networks seem to be a good source for this information. However, people think about their behavior in social networks: they choose what to post and weed out their thoughts. At the same time playing games we behave as we would like to in real life without thinking much. And for now, I managed to confirm that the game data is related to the real characteristics of people", notes Ivan Samborskii, a graduate student at ITMO University.
According to the scientists, the game data analysis can help to find out the interests, location, and demographics of users, as well as to assess how much time a person is willing to spend on games. Researchers will work to improve the resulting model making predictions about users more accurate. Also, they plan to adopt the model for prediction of the game addiction.
"On the Internet, the identity of user is unknown, and often we can only guess who is hiding under the avatar of the caustic commentator or under the nickname of the clan member. It is possible to lift the veil only by analyzing indirect signs, user's online behavior. Solving the riddle, who is on the other side of the monitor, is important both to the giants like Google, who get the main profit, correctly showing advertising, and to small online stores. An important and interesting question that arises in this case, what data is sufficient for this. In our past research, we used texts, images, and even geolocation. However, the players' behavior is described by a very special language of the hours spent in games and obtained achievements.
Our research has shown that even this information is enough to predict the players' gender. Certainly, we will not stop on this single feature, but now we have simply shown that game behavior can be analyzed and good predictive values ??can be obtained. In addition to the ubiquitous advertising personalization, good predictive models can be used in many applied research: sociological, psychological, sports and medical", adds Andrey Filchenkov, Head of the Machine Learning Group of the Computer Technologies laboratory at ITMO University.