Authors:
Leonardo Mauro Pereira Moraes
and
Robson Leonardo Ferreira Cordeiro
Affiliation:
Institute of Mathematics and Computer Sciences, University of São Paulo, Av. Trabalhador Sancarlense, 400, São Carlos, SP and Brazil
Keyword(s):
Data Mining, Social Networks of Games, Player Modeling, Classification, Feature Extraction, Data Streams.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Industrial Applications of Artificial Intelligence
;
Sensor Networks
;
Signal Processing
;
Soft Computing
Abstract:
Online games have become a popular form of entertainment, reaching millions of players. Among these players are the game influencers, that is, players with high influence in creating new trends by publishing online content (e.g., videos, blogs, forums). Other players follow the influencers to appreciate their game contents. In this sense, game companies invest in influencers to perform marketing for their products. However, how to identify the game influencers among millions of players of an online game? This paper proposes a framework to extract temporal aspects of the players’ actions, and then detect the game influencers by performing a classification analysis. Experiments with the well-known Super Mario Maker game, from Nintendo Inc., Kyoto, Japan, show that our approach is able to detect game influencers of different nations with high accuracy.